Asymmetrical multilateral decision support system

ABSTRACT

A system and methodology which can effectively provide decision makers with a better means of making decisions in a way that greatly improves the availability, reliability, and relevance of the information which they provide and use to make decisions. The system and methodology facilitates maximizing mutual utility in the context of a mutual decision between multiple users and groups of users identified generally as Parties and Counterparties and performs user specified actions based on meeting mutual threshold parameters. The system provides significant technical advantages over the prior art in that it uses helps Parties and Counterparties identify optimal arrangements and configurations with less errors, fewer computational cycles, less storage medium, and a smaller amount of time than would be possible using prior art systems.

This application is a continuation of U.S. application Ser. No.15/649,267 filed Jul. 13, 2017, which is a continuation of U.S.application Ser. No. 14/402,881 filed Nov. 21, 2014, now U.S. Pat. No.9,727,640, which is a 371 of PCT/US13/42787 filed May 27, 2013, which isa CIP of Ser. No. 13/480,529 filed May 25, 2012 now U.S. Pat. No.8,560,478, which are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention is directed generally to decision support systems,and, more particularly to systems and methodologies for implementingmutual decision making solutions in an effective manner.

BACKGROUND OF THE INVENTION

Considerable problems arise in performing decisions involving more thanone participant. Among the problems that exist are the availability (orlack thereof) of information, the reliability of that information, andthe relevance of that information to the needs of the decision makers.If the information fails in one or more respects it is highly likelythat a suboptimal decision will be made.

All decisions essentially rely on the ability of participants to predictthe outcomes of the decision from the inputs of the information whichled to the decision. While the abilities of various participants is notsomething that can likely be addressed through technology theavailability, reliability, and relevance of information whichfacilitates those decisions can be addressed through the use oftechnology.

There are several features of information systems which lead toinformation being unavailable, unreliable, and irrelevant. Thesefeatures prevent predictions (and thus successful decisions) from beingable to be made effectively by decision makers. In general theseundesirable results arise from one or more of the following categoriesof constraints: (i) unavailable information, (ii) unreliable informationand (iii) irrelevant information. These three categories of constraintscan be effectively described as information limits.

In contexts where information is limited, decision makers often have toaccept one of three bad options: (i) a high number of errors resultingfrom unavailable information (type I errors) or (ii) a high number oferrors resulting from unreliable errors (type II errors); or (iii) thedecisions made are significantly suboptimal by virtue of information andcoordination failure between decision participants. Type I errors limitthe downside of a potential decision but at the expense of a heavilyrestricted pool of decision candidates-which decreases the probabilitythat a really good decision can be made. Type II errors result inpotential decisions are not restricted to a narrow pool; however thatliberality comes at the cost of a higher probability that a truly baddecision has unknowingly been made. In each case, the decision maker'sinterests are not best served as the result of the limited information.The last option exists because decision makers aren't able to identifythe variant arrangements which could maximize the value of a decision.Consequently the results of the decision process are suboptimal to whatthey could have been.

There are several contributing factors for why information might belimited in the context of a mutual decision needing to be made. First,the more information a party or shares with the market regarding theirspecific interest, the greater the number of impostor counterpartiesappear masquerading as counterparty candidates which can meet thatinterest. Thus a party is reluctant to share a wide scope ofinformation. Second, the more information a market participant shareswith the market, the greater the information which is available forcompeting participants to use against them, further incentingparticipants to not share information. Lastly, the more information aparticipant shares with counterparty participants the greater leverage acounterparty participant has against the participant.

In addition, there are several problems which exist which makeinformation sharing and processing uneconomical for participants. Forexample, decision counterparts don't know the scope of the domain ofinformation which is critical for other participants to understand inorder to make a decision so they consequently provide large amounts ofirrelevant information which is very costly for participants to provide.Further, there isn't a common medium of gathering and communicatingcontextually similar information from decision participants who canprovide that information to decision participants who need thatinformation in order to effectively compare decision alternatives tomake a good decision. Another problem is that information that is sharedby counterparty participants is often too voluminous to be effectivelyprocessed. This information usually requires substantial additional timeand energy to process, filter, sort, and relate in order to make ituseful for a decision. Further, information provided by marketparticipants often contains several inaccuracies which make itunreliable. In order for participants to make the information reliable,significant time and energy must be spent verifying and cross-checkinginformation for inaccuracies so that the information a participant usesis reliable enough to use for a decision.

A brief sampling of examples of situations where bilateral andmultilateral decisions occur is as follows: (i) employment decisionsbetween potential employers and potential employees, (ii) servicing andcontracting decisions between service providers and contractors, (iii)real estate purchase or lease decisions made between owner/lessors andpossibly an owner/lessor's agent, and buyer/lessees and a buyer/lessee'sagent, (iv) industrial equipment decisions made between equipmentproviders and equipment users, (v) financial investment decisions madebetween financial services agents and investors, and (vii) supply chaindecisions made between designers, suppliers, integrators, logisticsproviders, and customers.

In each of these decisions all counterparties and parties involved inthe decision are engaged as stakeholders. Also involved are those thateach party or counterparty has designated to help them either (i)perform an evaluation or (ii) provide information associated with thedecision. In connection with each of these decisions, each party seekssomething that is typically only provided by another party.Consequently, the preferences which the parties have do not exist in asymmetrical fashion such that counterparty interests can reasonablyalign with one another or be compared with one another in meaningful waybecause even when interests do coincidentally align in one respect, thealignments of other preferences to attributes do not meaningfullyalign-meaning that in the aggregate there is no meaningful alignment ofinterests.

Beyond these practical challenges, there are also significant technicalchallenges which exist in the prior art which have not been previouslyaddressed. First among these is the significant number of computercycles which are necessary for decision support systems to performvarious functions. These functions include, but are not limited to: (1)survey generation, (2) survey scoring, (3) survey reporting, (4) surveyadministration, (5) data entry, (6) record creation, (7) data querying,(8) data evaluation, (9) scoring, (10) ranking, (11) sorting, (12)messaging, and (13) event triggering. Systems that require more computercycles to perform these functions require greater resources-including alarger number of computer processing elements, more electrical powerconsumption, and more space on electronic storage mediums in order toprocess such information.

Secondly, these systems require significant storage medium capacities inorder to process information. This is particularly acute with regard tomultilateral decision support systems. The prior art decision supportsystems aggregate tremendous numbers of records and use significantstorage resources, where information is stored to a storage medium.Within the domain of storage of data for decision support systems thesesystems also collect information which is often of low-value compared tostorage and maintenance costs of that information. This is primarily theresult of either the redundant collection of information or the failureof system design to select only information which primarily hashigh-value to be collected.

Thirdly, another failing of conventional decision support systems inthis area is that they may have very long temporal slack-periods betweencritical data inputs and the delivery of a final result which can beutilized in decision making. Fourth, many decision support systems areseverely limited in their ability to identify optimal arrangements ofParty-to-Counterparty arrangements due to the fact that their processeseither, (1) involve scoring based only on preferences withoutconsideration of attributes, (2) base their scoring only on the presenceor absence of attributes without relation to the preferences of decisionparticipants, (3) utilize unilateral evaluation where the preferences orattributes of either Parties or Counterparties are utilized as adecision criterion, and/or (4) suffer from a design that does notprovide sufficient flexibility to address either: (a) the rapid changesin the needs of decision participants, (b) the most relevant needs ofdecision participants, and/or (c) all of the various different types ofinformation which are needed by different types of decision participantsfor a given type of decision.

SUMMARY OF THE INVENTION

It is thus a primary object of the invention to provide a system andmethodology that addresses the shortcomings of the prior art asdiscussed above.

It is another object of the present invention to provide a system andmethodology which supports efficient decision making based upon theinformation available to the parties involved in the decision makingprocess.

It is a further object of the present invention to provide a system andmethodology to employ surveys containing attribute levels to developutility functions which aid in making the most effective decisions.

It is a still further object of the present invention to provide asystem and methodology which results in decisions which take intoaccount mutual preferences of parties involved in the decision so as topromote decisions which maximize the attributes desired by thecollective set of parties.

It is a further object of the present invention to provide a system andmethodology which minimizes the number of computer cycles, data entries,survey administrations, record creations, space required to storerecords, and the temporal distance between critical system events. Thereduction in these aforementioned directly and indirectly reducesresource consumption which are required by a system to supportdecisions-namely, they reduce the need for extensive computationalresources, space on storage mediums, and idle resource time wherecomputing resources can be utilized in decision making. The fact thatfewer of the above resources are used has a net result of requiringfewer physical processing units, less power consumption, less electronicstorage medium use, and less overall time for the system to performdecision support.

A primary objective the invention disclosed herein is a system andmethodology which can effectively provide decision makers with a bettermeans of making decisions in a way that greatly improves theavailability, reliability, and relevance of the information which theyprovide and use to make decisions. The system and methodologyfacilitates maximizing mutual utility in the context of a mutualdecision between multiple users and groups of users identified generallyas Parties and Counterparties and performs user specified actions basedon meeting mutual threshold parameters.

The disclosed system works by requiring that each user represent anentity and that each entity acts both as a survey respondent and assurvey evaluator. In this way each user/entity acts in the role of botha Party and a Counterparty. It should be understood that between two ormore entities it is not relevant which role is performed first in orderfor the system to perform its functions properly.

In one embodiment, the invention provides a means whereby a first set ofParties provide a series of attribute surveys to first set ofCounterparties. A second set of Parties (which also function as thefirst set of Counterparties) evaluates a second set of Counterparties(which also function the first set of Parties). In each case, each setof Counterparties completes a set of Parties' surveys by providingattribute levels for a given attribute survey as Survey Responses.

The attribute surveys provided by the first set of Parties isasymmetrical with respect to the attribute surveys provided by thesecond set of Parties-meaning that the surveys do not “mirror” oneanother nor do they necessarily relate to the same attributes. Thisdesign overcomes the problem of requiring a formal setup and design byan expert system designer and also overcomes the very narrow range ofapplicability which hinders the prior art.

According to a preferred embodiment of the invention, the systemprovides that each acceptable arrangement of Parties indicate theirpreferences for Counterparty responses at various attribute levels forvarious attributes through responding to forced-choice surveys. Throughapplication of conjoint analysis a utility function is derived givingeach Party's preferences for a given set of Counterparty's attributes ata given attribute level. The utility function provides not just eachParty's preferences for each attribute level at each attribute but italso provides each Party's preferences for various attributes relativeto one another and the preferences for various entities relative to oneanother which possess those attribute levels at each attribute invarious arrangements.

One unique aspect of the present invention is that it mutually evaluatesthe preferences of Parties for the attribute levels of the attributes ofCounterparties for each role in a decision as opposed to evaluating onlythe preferences of Parties against the preferences of Counterparties orotherwise unilaterally evaluating the preferences of Parties for theattribute levels of the attributes of Counterparties.

This provides the advantage of extending the range of possiblearrangements to which a conjoint analysis or other preferenceelicitation method can be applied in order to create a vastly largernumber of mutually beneficial arrangements between Parties andCounterparties acting in various counterpart roles.

An additional unique aspect of the present invention is that it allowsboth Parties and Counterparties to incorporate within their evaluationsand responses respectively the evaluation of Co-evaluators andCo-respondents in each case which provides for a multilateralarrangement of evaluation and response which is more encompassing thanthose which exist in the prior art because the decision support systemincorporates a greater degree of decision participants which act asstakeholders in any decision than is available in the prior art.

An evaluation is performed wherein each Entity participating in adecision, acting in a first role, evaluates each Entity participating inthe same decision, acting in a second role or other role. Likewise eachEntity acting in a second or other role evaluates each Entity acting ina first or other role. This mutual evaluation of Entities in roles canbe performed for as many roles as there are for each decision.

Accordingly, a first evaluation is performed wherein an Entity acting ina first role, which we here identify as a Party, evaluates an Entityacting in a second or other role, which we here identify as aCounterparty. The Party's utility function is evaluated at eachCounterparty's attribute levels for each attribute. The result of allthese evaluations for each Counterparty's attributes is aggregated toyield a total utility which the Party has for a Counterparty.

Likewise, a second evaluation is performed wherein an Entity acting in asecond or other role, which we here identify as a Party, evaluates anEntity acting in a first role, which we here identify as a Counterparty.The Party's utility function is evaluated at each Counterparty'sattribute levels for each attribute. The result of all these evaluationsfor each Counterparty's attributes is aggregated to yield a totalutility which the Party has for a Counterparty.

If more roles exist for a given decision additional role evaluations areperformed wherein each Entity acting in a given role performs thefunction of a Party by evaluating each other counterpart Entities actingin counterpart roles, which likewise perform the function in each caseof a Counterparty.

The combined total of each mutual arrangement of evaluations of Entitiesacting in counterpart roles is totaled for each arrangement. The resultof these evaluations is a list of mutual utilitypreferences-for-attribute values where each Party has given eachCounterparty a utility score for each mutual arrangement. For eachunique combination of Party and Counterparty arrangements a singleresulting utility value exists which can be provided to users as anindex of mutual utility values which exist between any arrangement ofcounterpart Entities involved in a decision where the Entities act incounterpart roles. These values are provided to users as a sorted listof mutual utilities. This means that if there were 30 first Entitiesacting in a first role and 50 Entities acting in a second role and eachacceptable arrangement consisted of only one Entity in a first role andone Entity in a second role then the resulting list would consist of1500 unique combinations of arrangements from which users would selectthe best arrangements for themselves. Accordingly if there were threecounterpart roles where there were 30 Entities in a first role, 50Entities in a second role, and 12 Entities in a third role where therewould only be one Entity selected from each role, then the resultinglist would consist of 18000 unique combinations of arrangements fromwhich users would select the best arrangements for themselves.

Users of the system can utilize mutual preference-for-attributeutilities in order to make better decisions by performing comparativeevaluations that would be infeasible for humans to perform alone. Thesemutual preference-for-attribute utilities are employed in order toperform a series of actions which have been designed by each Party if agiven set of threshold conditions are met.

By way of example and not of limitation, the teachings of the presentinvention can be applied in the context of the requirement for a mutualdecision of a potential employer acting in a first role and that of apotential employee acting in a second role. This invention can beapplied to such employment decisions where labor can be identified as afirst role and management can be identified as a second rolerespectively making an employment decision in each role where the actionwhich might be taken would be to execute an employment contract.

As just another example, this invention can be applied to contractingand/or servicing decision where a contractor and/or service recipientcan be identified as a first role and a subcontractor and/or serviceprovider can be identified as second role, respectively, in acontracting and/or servicing decision where the action performed wouldbe the submission and acceptance of a proposal respectively. In similarfashion, this invention can also be applied to an industrial equipmentdecision where a provider of industrial equipment could be identified asfirst role and the user of the industrial equipment could be identifiedas second role, respectively, in an industrial equipment decision wherethe action might be the sharing of information for the creation of anagreement.

The teachings of the present invention could also used to facilitatedecisions between buyers and sellers in a transaction decision ofpersonal property, real estate, financial instruments or intellectualproperty where the buyer would be the first role and the seller would bethe second role, respectively, in a transaction decision. Furthermorethe invention could also be used to facilitate mutual selection orcommunication decisions between institutions and individuals, groups andindividuals as well as one individual and another individual where ineach case the former would be considered a first role and the latterwould be considered a second role respectively in a selection decision.

The applicability of the invention is not limited to merely arrangementsof two role counterpart pairs. A mutual decision may involve severalParties and Counterparties role arrangements in any given arrangementssuch as a supply chain involving several suppliers, integrators,distributors, and retailers, all within one or more supply-chaindecisions where a number of various role relationships beyond merely twoexist.

In connection with any mutual decision, either Entity is a Party andCounterparty and can effectively be identified as either a first role orsecond role. Thus, the rest of this disclosure will simply refer to eachEntity by the role in which they are acting as either Party andCounterparty in each case unless additional clarification is needed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is diagram depicting the major components of the system of thepresent invention in a preferred embodiment thereof;

FIG. 2 is a flowchart illustrating the methodology of the presentinvention at a high level in a preferred embodiment thereof;

FIGS. 3A and 3B provide a general mathematical overview of the majorfunctions of the system of the present invention in a preferredembodiment thereof;

FIG. 4 is an illustration showing the relationship of various Entitiesacting as Parties and Counterparties to one another and in various rolesin connection with a given decision;

FIG. 5 is a flowchart illustrating the steps undertaken by the system ofthe present invention in connection with the Parties review, selectionand creation of surveys for Respondents to complete according to apreferred embodiment of the present invention;

FIG. 6 is a flowchart illustrating the process of survey optimizationwhereby surveys are evaluated, promoted, and depreciated based on theirrelative relevance to the needs of both Parties and Counterparties;

FIG. 7 is an illustration of an exemplary survey relevance scoringevaluation;

FIG. 8 is an illustration of an exemplary survey hierarchy;

FIG. 9 is a flowchart illustrating the process for gathering surveyresponses from Respondents according to a preferred embodiment of theinvention;

FIGS. 10A and 10B is an illustration of an example of row and columnevaluations which are performed such that Evaluators rate the SurveyResponses of certain attributes in the row evaluation and Entities inthe column evaluation;

FIG. 11 is a flowchart illustrating the process whereby vote aggregationrules are selected and applied to create utility functionsrepresentative of more than one User;

FIG. 12A is a flowchart illustrating the process whereby Entities modifyand update various elements associated with their Entity;

FIG. 12B is a flowchart illustrating the process through which when agiven change occurs within the system, the evaluation of the results arerecalculated and re-evaluated; and

FIG. 13 is a flowchart illustrating the process whereby the system ofthe present invention evaluates criteria for subject Entities to performActions to object Entities and for subject Entities to allow objectEntities to perform actions to the subject Entity.

DETAILED DESCRIPTION OF THE INVENTION

In connection with the following disclosure, the following definitionsand notational conventions will be used:

A. Definitions

User: Is a natural person who uses the system by interacting with one ormore elements of the interface of the system.

Attribute: Is a characteristic of a tangible or intangible objectconsisting of its real or perceived qualities.

Attribute Level: Is a quantitative, qualitative, descriptive, binary orcategorical value that is associated with an Attribute.

Entity: Is an object which can be engaged in a transaction of mutualvalue, comprising of an individual, or a collection of individuals, oran organization, or collection of organizations and one or more andtangible or intangible goods, services, or qualities which can betransacted.

Party: Is a classification which identifies an Entity member of a firstclass which evaluates a second class of Entities (Counterparties). AParty can be thought of as any Entity which is a subject which performsevaluations of members of object Entity class (Counterparty). It shouldbe noted that because the evaluation of Entities is mutual Entities canbe simultaneously be members of the classification of Party, whichevaluates, and of Counterparties, which are evaluated.

Counterparty: Is a classification which identifies an Entity as memberof a second class of Entities which is evaluated by a member of a firstclass of Entities (Parties). A Counterparty can be thought of as anyEntity which is evaluated by a member of a subject Entity class (Party).

Evaluator: Is a User which is a member of a Party or Co-Evaluatorclassification that evaluates one or more Counterparties or surveyresponses.

Co-evaluator: Is a User associated with a Party which assists in theevaluation of one or more Counterparties or survey responses.

Respondent: Is a User which is a member of a Counterparty orCo-Respondent classification that responds to one or more surveys.

Co-respondent: A User that provides information either related to aCounterparty or in behalf of a Counterparty by verifying, improving orcompleting one or more surveys.

Survey Response: Is a response made by a Respondent to one or more Partysurveys which indicates an Attribute Level of an Attribute. A SurveyResponse may also include an Attribute Level datum or data for anAttribute which is provided by a Counterparty or Co-Respondent directlyover a communication network which is acting in the role as a SurveyResponse but which may not strictly be a response to a survey providedby the system.

Dummy Responses: Is a response presented to an Evaluator designed toelicit a preference which contains dummy variables where no real data ispresent.

Preference Response: Is a response which yields a preference evaluationof one or more of the following in response to a Survey Response orDummy Response: (i) an attribute level with respect to other attributelevels within a given attribute category, (ii) an attribute with respectto other attributes, and (iii) a collection of attributes with respectto other collections of attributes.

Utility Function: A set of one or more mathematical functions whichpredicts the utility value which a Party or Evaluator would assign agiven Survey Response, set of Survey Responses, Attributes, AttributeLevels, set of Attributes, or set of Attribute Levels from a set ofPreference Responses which a Party or Evaluator has provided.

B. Notational Conventions

There are several mathematical representations throughout thisdisclosure. The first is the representation of a generic variable of anykind which is represented as a bracketed bullet, (•). These bulletsserve as placeholders for other variables such that the other notationalconventions can be explained. Several different kinds of variables areprovided throughout the disclosure. Variables belonging to the class ofParties are noted as variables without hats (•) and variables belongingto the class of Counterparties are noted with hats ({circumflex over(•)}). For the purposes of clarity, the variable (e) is identified anEntity which is a member of a Party whereas (ê) is an Entity which is amember of a Counterparty. When discussing variables, (brackets) anditalicized letters are generally used to identify them if they appearwithin text.

To further distinguish types of variables the convention of theover-script prime, ({acute over (•)}), and over-script double prime, (

), are used in order to identify differences between Respondents andEvaluators. Respondents and Co-Respondents are identified by anover-script prime ({acute over (•)}) and Evaluators and Co-Evaluatorsare identified by an over-script double prime (

).

Within the disclosure, the notation (Q_(e) _(s) ) is used to identifythe surveys requested by Parties for Respondents to complete. The (Q)identifies that the variable is a survey and the subscript (e)identifies the Entity source and the sub-subscript (s) identifies thesurvey number from a given Entity. Thus (Q₁₅ ₃ ) represents surveynumber 3 of Party Entity number 15.

With reference now to FIG. 1, the system of the present invention, in apreferred embodiment thereof, is now described. System 1000 ispreferably a computer based system for implementing the functionality ofthe present invention as described in greater detail below. While anexemplary architecture is described, it will readily be understood byone of skill in the art, that an unlimited number of architectures andcomputing environments are possible while still remaining within thescope and spirit of the present invention.

Preferably, a number of Party Interfaces 1050 are made available forUser use so that such Users can interact with System 1000 as describedbelow. Party Interfaces 1050 may be any devices as is known in the artthat allow User input and the ability for the User to receive data,information and reports from System 1000 as well as to interact withSystem 1000. By way of example and not by limitation, Party Interfacesmay be laptop or desktop computers or terminals, or mobile devices suchas tablet computers, smartphones and the like. Communication betweensuch Party Interface devices 1050 and System 1000 may be via a wirelessconnection or via a wired connection as is known in the art. PartyInterfaces 1050 may be geographically dispersed and there may be a largenumber thereof so as to allow a large number of Users associated withvarious Entities to interact with System 1000 as desired or asnecessary.

Processing associated with interacting with Users via Party Interfaces1050 is generally managed by User Interface Module 1060. Suchfunctionality may include receiving and formatting responses andrequests from Users via Party Interfaces 1050, generating graphical userinterfaces for display on Party Interfaces 1050 and managing andsoliciting User input as required by the functionality of System 1000.

User Interface Module 1060 (as well as each of the other key modulesdescribed below) communicates with Central Processor 1020. CentralProcessor 1020 provides primary operational control over all processesand functionality implemented by System 1000. Central processor 1020(and some or all of the other components described herein in connectionwith System 1000) may be implemented as a server based computingplatform which is robust and readily expandable as processingrequirements expand. Central Processor 1020 also communicates withStorage 1010 for storing and retrieving data as generated and as neededby System 1000 to implement the teachings of the present invention asdescribed more fully below. Storage 1010 may be any suitable storagefunctionality as is known in the art, such as for example, disk basedstorage that provides sufficient data capacity and readily availableaccess to data as needed.

Survey Management Module 1030 also interacts with and is under thecontrol of Central Processor 1020. Survey Management Module 1030provides all functionality associated with soliciting input from Usersas required in connection with the generation, modification andcommunication of surveys between and among Users associated with variousEntities as more fully described below. Utility Function Generator 1040communicates with Central Processor 1020 and serves to manage allprocesses associated with generating utility functions based on Entitypreferences and survey attributes and responses as more fully describedbelow.

Finally, Evaluation and Scoring Module 1070, under the direction ofCentral Processor 1020, manages and implements all processes associatedwith the evaluation of Parties/Counterparties in connection with thedecision support functionality described in detail below.

Now that the system and related components of the present invention havebeen described, the following provides a detailed disclosure of theprocesses and functions of the present invention in a preferredembodiment thereof.

1: Select Decision & Role

With reference to FIG. 2 (which provides an illustrative overview of theprimary functionality of the present invention) along with FIG. 3A andFIG. 3B (collectively FIG. 3) and FIG. 4, the high level operationalelements of the present invention are now described.

At FIG. 2 [step 10], System 1000 provides the Parties with a means ofperforming a decision using one or more computing devices over acommunications network. A selection of decisions and roles are madeavailable for Users to search and select. Once one or more decisions androles have been selected, other Entities select the same decisions andcounterpart roles necessary to make a decision.

This selection is performed first by Users conducting a search ofdecisions and roles which are available to be selected from an existinglist of available decisions and roles. Second, the list of decisions androles is returned to the User and the User chooses either, (i) to selectone or more existing decisions and roles which counterpart objectEntities have defined which is compatible with the interests of thesubject Entity, or (ii) create a new decision and assign for themselvesa role and one or more counterpart decision roles which will befulfilled by other counterpart Entities.

If (ii) has been selected, the details of that decision and itsattendant roles are provided and are made available for other Entitiesto search, review and select. Third, other Entities select a decisionand a role where one or more Entities have selected the same decisionand a counterpart role necessary for all the roles within a givendecision to be filled. Each counterpart role for each decision may beoccupied by one or more Entities participating in a decision such thatmultiple Entities can participate in a given decision at any one time.It should be obvious to anyone skilled in the art that which Entitiesselect which decisions or roles first is not important since either canbe selected prior to the others. It should also be obvious to anyoneskilled in the art that at any given period of time there may be severalorphaned decisions which are waiting for decision role counterparts toselect the same decision.

Step 10 is performed by first obtaining from a Party the decision theentity with which they are associated would like to make and the rolethat entity would like to play within that decision in addition to theroles that entity would like other entities to play in the samedecision. These decisions involve more than one role in order for anexchange of value to take place within each decision. These decisionsmay be varied. Some examples of decisions include but are not limitedto: employment decisions, contracting and/or servicing decisions, realestate decisions, industrial equipment decisions, financial investmentdecisions, buyer and seller transactional decisions, personal propertydecisions, mutual selection decisions and communication decisions.

Decisions and roles related to them are described, selected,categorized, sub-categorized, and modified by decision participants suchthat no specific expert or existent classification of decisions isneeded. This lack of an arbiter or a specified collection ofstandardized decisions which can be performed allows for the flexibleconfiguration and reconfiguration of decisions to be made. The decisionsmay have a network structure with nodes and edges or alternatively ataxonomical structure with classifications and sub-classificationswithin a taxonomical tree such that individuals can discriminate betweenvarious types of decisions on the basis of the selection of variouselements within the given decision structure.

The advantages of these various network and taxonomical structures arethat they allow for useful elements of a set or subsets of decisions tobe used in other decisions. Additionally the use of network andtaxonomical structures within decisions allows for sets and subsets ofdecisions to be compared with one another in ways that increases theoverall value of a given exchange.

In an example, a potential employer elects to perform an employmentdecision. He or she also selects a subcategory of an employmentdecision, which is mathematical computer programming decision. Apotential employee also elects to perform an employment decision. He orshe selects a subcategory of computer systems engineering programming(which is closely related to mathematical computer programming in thiscase). Because of the categorical structure of these decisions, certainelements which may be mutually beneficial to both a potential employerand a potential employee might be compared whereas they might not havebeen be obvious to either previously.

One illustrative overview in FIG. 4 helps to illustrate the relationshipof the Entities to one another in a mutual decision. It should beobvious to one skilled in the art that any number of roles can berepresented in this evaluation. FIG. 4 represents only three; however,any mutual decision may involve any number of roles greater than two andany number of entities acting in various roles.

Furthermore the sequence of these evaluations may be made in any order.

2: Get Party Surveys

With reference to FIG. 2 [step 20], the system of the invention allowsParties to encode their unique criteria for making a decision into twoparts which can then be processed and analyzed by one or more computingdevices in order to identify the ideal mutual arrangement between aParty and a one or more Counterparties. The two parts of the criteriathat Parties encode are: (i) the information necessary for making adecision FIG. 3A [step 100] and (ii) the actual preferences which theyhave for and between various attributes and attribute qualities whichare evaluated FIG. 3 [step 103].

These two criteria are provided to the System 1000 by Parties andCo-Evaluators. They provide this as (i) a set of surveys, FIG. 3A [step100] applied to a given role, which are used for accumulating theresponses from Counterparties and Co-Respondents which are thenevaluated and as (ii) a series of preference responses, FIG. 3A [step103], that capture the relative preferences which Parties andCo-Evaluators have for various attributes relative to other attributesat various attribute levels relative to other levels as well aspreferences which Parties and Co-Evaluators have for entities whichpossess given sets of attributes at given sets of attribute levels. Itshould be noted that FIG. 2A [step 20] and FIG. 3 [step 100] correspondto the same activity and FIG. 2 [step 40] and FIG. 3A [step 103] alsocorrespond to the same activity.

System 1000 provides a means for Parties to select and provide surveysto the system which are most appropriate to their individual needs. Thismechanism provides Parties with the flexibility to acquire anyinformation from a Counterparty without the need for the explicit inputof a system designer in order to facilitate a decision. The benefit ofthis system over the prior art is that System 1000 reduces the aggregatenumber of survey administrations and data entries which need to beperformed by the system in order to capture this highly valuable data.This reduces the number of computer cycles performed by the system byeliminating administration of duplicate surveys to Respondents. Thisfurther reduces the number of responses which are collected and stored,and subsequently the amount of storage which is necessary to store theseresponses in order for a decision support system to perform itsfunction.

To further describe this technical feature in FIG. 3A [step 100], aParty encodes the information necessary for them to make a decision byselecting appropriate surveys for Respondents to complete for a givenrole. The notation used in the diagram of (Q_(m) _(n) ) provides that(Q) identifies the variable as a survey, the subscript (m) identifiesthe entity which generated the survey and the sub-subscript (n)identifies the survey which among a number of surveys was generated bythe entity.

Throughout this disclosure, surveys will be also be mathematicallyrepresented as where the (e) can be 1−m values and the (s) can be 1−nvalues:

Q _(e) _(s)   [2.01]

The block diagram of FIG. 5 illustrates the steps involved in creating asurvey in more detail and outlines the module which provides the meansnecessary to reduce the number of computing cycles by a system andstorage medium consumed by the system. The precise detail by which thisreduction in resources is accomplished is identified in Section 4 and isfurther detailed in Equations [4.02, 4.03, 4.04 and Table 11].

At [step 210] Party Entities search a database for existing surveyswhich if answered may facilitate a decision. System 1000 returns a listof existing surveys to the Party from the query. Parties then review thedescriptions of surveys which may be useful, along with the actualsurveys themselves, and a SUS (survey utility score) for each survey fora given role and a given decision.

Parties perform this search in order to minimize the effort of creatingnew surveys (which can be time-consuming) and in order to encourageParties and Counterparties to standardize the use of best-of breedsurveys. Parties may create non-standard surveys by encoding theinformation they seek to obtain from Counterparties in one of threeways: (i) by selecting an existing survey which has been created byother Parties, (ii) by creating a new survey, or (iii) by selectingexisting surveys and then modifying one or more of elements of thesurvey to better suit the specific needs of Party-effectively creating anew survey.

At [step 215] the Party, having reviewed the various surveys, determinesif one or more of the surveys approximately meets his/her needs touncover information which Respondents will provide. At [step 225] if theParty determines that no survey meets its general requirements then theParty undertakes to create a new survey. The Party specifies for thesystem the following elements of the survey: (a) the questions orqueries which are posed by a survey, (b) the information presented tothe Respondent for response (such as information which might provide aRespondent with context in order to provide the appropriate informationin a response), (c) the data requirements for a response to consideredvalid (such as whether data is quantitative, qualitative, descriptive,binary or categorical), (d) what threshold conditions must be met inorder for a given data entry to be considered valid by the system (oneexample of the aforementioned is a threshold condition exists where aParty wants only responses that include categories that are not “N/A” or“Blank”; another example would be that a response provided by aCounterparty that is not verified by a Co-Respondent would be consideredinvalid; another example would be that a response provided by aCo-Respondent which does not provide a valid auditing information andauthentication for the Co-Respondent is not considered valid).

Within each survey there is an option to disclose information which isconfidential to the Party. The disclosure of confidential information aspart of the survey is accompanied by the ability which the Party has ofdefining the criteria for disclosing this confidential information toRespondents. If the Party defines confidential information within thesurvey they must also define the criteria for disclosing thisinformation. The criteria for disclosure may include having completedanother identified survey (such as a confidentiality agreement or asurvey which verifies that they have an active security clearance).Examples of this kind of information may be in the case of a contractingdecision where certain confidential information is necessary forCounterparties to understand the scope of the work and respond to thesurveys appropriately. However, the Party and can only disclose theconfidential information to relevant Entities that have completed anon-disclosure agreement. Relevant counterparties may therefore berequired to meet a series of conditions which identifies them as havingcompleted such a non-disclosure agreement prior to accessing theconfidential information.

At [step 215] if one or more of the surveys provided does generally meetthe Party's needs then they are selected by the Party to determine ifthe survey should be further improved in order to better meet the needsof the Party or whether it should be selected outright. At [step 220] ifthe Party determines the survey should not be improved, because themarginal value added by survey modification generally would not begreater than the value of the effort required in order to modify thesurvey then no elements of the survey are modified and the survey isselected as it is. At [step 230] if the Party determines the surveyshould be improved then it is modified. The modification of the surveymodifies one or more elements which have been indicated above aselements of the survey. At [step 235] the Party then provides anexplicit rating of the survey which numerically identifies its relativeimportance to other surveys.

At [step 255] the Party next determines the type of Respondent thatshould complete the survey. A survey can be completed by either a (i) aCounterparty or by (ii) a Co-Respondent. In some cases the response of aCounterparty provides more useful information than a Co-Respondent andin other cases the response of a Co-Respondent provides more usefulinformation than a Counterparty. (For example in an employment decision,the response of a potential employee to a survey question, “Rate howeasy others get along with this person from 0-9” is likely to producedifferent results depending on whether the prospective employee is askedversus that of a relatively objective co-worker. Likewise there aredifferent responses which may be produced by Counterparties andCo-Evaluators in an investment decision where the roles of the decisionmakers are investor and broker. A response by a counterparty brokerregarding the relative riskiness versus its returns for an investmentwould be different than that of a disinterested third-party analyst.)

At [step 260] if a Co-Respondent is selected to complete the survey thenthe Party next determines who will select the Co-Respondent. In somecases it is useful for a Party to select a Co-Respondent which mustcomplete the Survey Response. (One such example might be a contractingdecision where the Counterparty subcontractor needs to have securityclearance in order to work on a project; the Co-Respondent might be thesecurity clearance provider or clearinghouse that can verify thesecurity clearance of the subcontractor. Another example for anemployment decision might be where a Party potential employee would needa Counterparty employer to possess verification from a particularthird-party ratings agency regarding the stability of a financialhistory before a certain employee would consider working for them.)

At [step 265] if the Co-Respondent is selected by the Party, then theParty provides the necessary contact information for the system to reachone or more Co-Respondents to complete the Survey Response. If thecontact information necessary for collecting the Co-Respondent's surveyresponse already exists within the system the Party may be able toselect it [step 270]. In other cases it is useful for Counterparties toprovide Co-Respondents which cannot conveniently be determined by theParty. (One such example might be for an employment decision where aParty wants a Co-Respondent to provide details regarding the specificeducational background of the Counterparty but does not necessarily carefrom which education institution Survey Response is provided. Anotherexample might be for a servicing decision where a Party contractor wantsformer clients of a subcontractor to rate the relative satisfaction theyhad in their dealings with the subcontractor but the Party contractor isnot necessarily interested in which specific clients the subcontractoruses to complete the Survey Response.)

At [step 275] for each survey that a Party requests a Respondentcomplete, there is the option to have the survey be qualified by anadditional subordinate qualification survey. The qualification surveyprovides a means of Parties qualifying the responses of Respondents suchthat the Survey Responses of one Respondent may be insufficient to makea decision and additional qualifying Responses may need to be providedin order for a Party to have adequate information to make a decision.(For instance in a an investment decision if a Party investor had aCounterparty investment company provide its entire financial statementwithin a survey that survey may by itself be insufficient because itcannot be verified. The Party would therefore create a qualifying surveywhich would have an auditor provide an audit opinion of the financialstatements such that the Party could then make an investment decision.The state of the auditor's opinion would qualify the financial statementprovided by the Counterparty Respondent. In another example in a RealEstate decision a Counterparty property owner might claim that aproperty has a particular zoning which allows for its development;however, the statement of the property owner alone might be inadequatefor a Party to make a decision, therefore the statement of the propertyowner would need to be qualified by either a Co-Respondent governmentauthority or an engineering firm which could verify the accuracy ofthose details.)

At [step 280] if the survey is qualified by another survey then thedetails of the qualifying survey would be entered just as the surveybeing qualified and would follow the same procedure, even allowing forsubordinating qualifying surveys which would qualify it. At [step 290]if the survey is qualified no further by any qualifying surveys then thesurvey is closed and stored to a digitally programmable medium.

2A: Survey Optimization

One of the problems with allowing so many Parties to encode their uniquedecision criteria for Counterparties is that Parties will create an everexpanding number of surveys which Counterparties will have to complete.The problem with creating additional surveys which are proximallysimilar is that it burdens each Respondent with the task of completingeach and every additional survey without providing marginal aggregatevalue per unit of effort. In fact it can make the decision processsignificantly less efficient for all participants merely because onedecision participant desires to specify a small survey difference thatmay provide him/her with a marginal degree of value.

To combat this potential problem, two features which can collectively bereferred to as a genetic optimization process are introduced which helpminimize this problem.

These features have the technical effect of reducing the amount of datawhich needs to be stored by a decision support system on a storagemedium as well as reducing the amount of computational power which isneeded to administer surveys to respondents. This also facilitates thesystem to store only primarily high-value information. This is done inthe following manner. First, a feedback system is employed to helpEvaluators and Respondents coordinate the use of a limited set ofsurveys which approximates the greatest quantity of value for a finitedegree of time and energy spent on any given decision. This consequentlyenables Entities to focus their time and efforts on selecting andcompleting only the surveys which are most useful for a givendecision-which also reduces the number or surveys which need to beadministered to Respondents and the amount of storage space which needsto be utilized in order to store the responses of Respondents. Thisfeedback system employs (i) a Survey Utility Score (SUS) whichidentifies for Respondents both the relative value of a given surveyrelative to other surveys and the degree which a given survey is used byEvaluators and other Respondents, and (ii) information which relates toParties the number or percentage of Respondents who have completed agiven survey within a given decision and role population, whichidentifies for each Party how immediately useful a given survey will be.Second, the selection of the number of surveys is limited-meaning that aset of surveys which best meet the fitness criteria of both Evaluatorsand Respondents is provided. Accordingly, second, Parties are limited inthe number of surveys they can select to have Respondents complete by acost function which is applied to them by the system-meaning that themore surveys they request Respondents complete the greater the totalcost that is applied to them. Respondents are likewise limited in thesurveys they select, but for different reasons, because each survey theycomplete requires a degree of time, thought, and effort to complete.Consequently, it may be impractical to complete more than a certainnumber of surveys. Being able to select which surveys they complete anddo not complete they have an incentive to complete the surveys theybelieve are the most relevant to Evaluators.

Together these two forms of feedback provide a measure of “fitness”which can be employed by each type of Entity as to evaluate forselection as well as a means of effective population reduction, whichdoes not limit the actual population of surveys, but instead limits thepopulation of useful surveys for any given period of time. Surveys whichdo not meet these fitness criteria for selection are iterativelydepreciated through the feedback process and their selection is replacedwith those that better meet the measures of fitness criteria. Thisevaluation for selection ultimately yields an optimal limitedarrangement of Surveys for each role in a decision which can becollectively used by both Parties in order to undertake evaluation for adecision.

This process is illustrated in the block diagram of FIG. 6 where [step305] requires that a survey is returned from a database query the Entityhas made. The survey returned from the database may be a list of surveyswhich are presented to the Entity and reviewed and compared in thefollowing block simultaneously. At [step 310] the system identifieswhere the Entity is provided with both the survey content to review aswell as the information which provides the relative fitness scores whichother Entities have assigned to the Survey through selection andevaluation. These steps [305] and [310]correspond to FIG. 5 step [210].At [step 315], for each Entity they must select for themselves whetherto choose the survey relative to other surveys with the information theyhave been provided.

In the case of a Party selecting a survey, they are limited by selectinga finite quantity of surveys for Respondents to complete. This is thesurvey threshold identified in [step 335]. Evaluators must thereforebudget their survey selections and choose the best surveys to select inorder to yield the optimal results. In practice this means that Partieseither select the survey on the basis that it provides better overallutility than other surveys, forego collecting the information which wasidentified by the survey-indicating to the system that the survey'sutility value was lower than that of other available surveys.

In the case of Respondents, each respondent selects those surveys whichbest provide them with the greatest possible chance of achieving anoptimal decision for a given level of effort they are willing to providewithin the survey budget they are allocated

At [step 320] in FIG. 6, if the survey is not selected an SUS of (0) isassigned to the survey from that entity and the Entity must selectanother survey. In the case of an Evaluator, [step 320] corresponds tosteps [225] and [230] of FIG. 5, which provide the means of improvingthe pool of surveys which are available to a given population ofEntities. These surveys must then be reviewed, selected, and evaluatedin order to improve their relative “fitness” to the population throughevaluation and use otherwise they are depreciated.

At [step 325], each Entity evaluates the survey for relevance andprovides a singular value which identifies the perceived value of theutility of the survey related to all other surveys for the same role inthe same decision. At [step 330], each time an Entity performs an actionwhich may change the aggregate value of the SUS for a given survey it isrevaluated by the system and a new SUS evaluation is computed. At [step335], the system determines whether a given threshold value has beenreached which limits further evaluation selection. If that threshold hasbeen reached the system ends the evaluation for that Entity. If thethreshold has not been reached the Entity may continue to search andselect additional surveys.

2B: Survey Utility Scoring

FIG. 7 provides an example of an evaluation of survey relevance bycompleting the function necessary for generating a relevance score foreach survey which identifies the relative utility of each survey.

First, Entities explicitly provide an initial Survey Relevance Score(SRS) from a range of values relatively which is rescaled to berelatively equivalent to the same values which are provided by theutility function. Second, the Entity is assigned a weight by the systemwhich identifies the degree of importance their evaluation is to aparticular survey relative to all other Entities. This is called itsopinion weight. This weight is assigned by the system based on a varietyof factors specific to each implementation of the system.

In the case of an employment decision, the weight of an Entity might bebased how many employment decisions were successfully conducted over theperiod of the previous two years. In the case of an industrial equipmentdecision the weight might be based on the average contract size of theorganization associated with the Entity for some period of time, oralternatively the size of average annual economic transaction volumewhich is undertaken by a firm. In the case of a real estate decision,the weight might be based on the number of total acres which a firmmight possess. The weights for other types of Entities are assigned bythe system according to relevant standards.

Third, the SRS and the derived utility of each survey is given byrelevant Entities for a given role in a given decision which is combinedto create a survey utility score (SUS). This evaluation is performed asfollows:

For the first sum, each Party's derived utility for a particular surveyor their SRS is used for a particular survey which is then multiplied bythat Party's weight; the product of this operation is then totaled. Forthe second sum, each Party's derived utility for all surveys or theirSRS for all surveys is multiplied by that Party's weight; the product ofthis operation is totaled. The first sum is divided by the second sum toyield a weighted average of the opinions which Parties have for a givensurvey in a given role for a given decision.

The Survey Utility Score (SUS) is mathematically expressed as follows:

$\begin{matrix}{{\Lambda {()}}:={{\left( \frac{\overset{i,l}{\sum\limits_{{({\overset{''}{e},\overset{\overset{\prime}{\hat{}}}{e}})} = 1}}{\left( {{U_{\overset{''}{e}}\left( {{\hat{A}}_{\overset{\overset{\prime}{\hat{}}}{e}}{()}} \right)}w_{\overset{''}{e}}} \right)\bigvee\left( {{{SRS}_{\overset{''}{e}}\left( {{\hat{A}}_{\overset{\overset{\prime}{\hat{}}}{e}}{()}} \right)}w_{\overset{''}{e}}} \right)}}{\overset{i,j,k,l}{\sum\limits_{{({\overset{''}{e},e,s,\overset{\overset{\prime}{\hat{}}}{e}})} = 1}}{\left( {{U_{\overset{''}{e}}\left( {{\hat{A}}_{\overset{\overset{\prime}{\hat{}}}{e}}\left( Q_{e_{s}} \right)} \right)}w_{\overset{''}{e}}} \right)\bigvee\left( {{{SRS}_{\overset{''}{e}}\left( {{\hat{A}}_{\overset{\overset{\prime}{\hat{}}}{e}}\left( Q_{e_{s}} \right)} \right)}w_{\overset{''}{e}}} \right)}} \right)w_{\overset{''}{e}}} + {\left( \frac{\sum\limits_{{(\overset{\overset{\prime}{\hat{}}}{e})} = 1}^{l}{{SRS}_{\overset{\prime}{\hat{e}}}{()}}}{\sum\limits_{{({e,s,\overset{\overset{\prime}{\hat{}}}{e}})} = 1}^{j,k,l}{{SRS}_{\overset{\prime}{\hat{e}}}\left( Q_{e_{s}} \right)}} \right)w_{\overset{\overset{\prime}{\hat{}}}{e}}}}} & \lbrack 2.02\rbrack\end{matrix}$

Where the utility function of a given Party, (

), for a given survey, (Q_(e) _(s) ), is indicated by (

(

(Q_(e) _(s) )).The OR operator, (V), is used to indicate a choice of operationsdepending on whether the utility function or the SRS provides the bestevaluation of a Party's relevance. The SRS for a given Party is SR

(

(Q_(e) _(s) )) and the opinion weight of a Party is indicated by (

). Likewise the SRS of a given Respondent is given (SR

(Q_(e) _(s) )), and opinion weight of the Counterparty or Counterpartiesis indicated by (

). In this case the superscripted breve ({hacek over (•)}) over the(Q_(e) _(s) ) element indicates a separation between the first andsecond sums which are performed identifying the breved element as theelement for target analysis. In general the evaluation can be performedevaluate the discrete sum which all Parties have for a given surveyagainst the value which they have for all surveys for a given role in agiven decision.

A brief proof of the increase in computational and storage efficiency isprovided here to demonstrate the net-effect of the use of thegenetic-optimization process on the total efficiency of decision supportsystems. If a decision support system lacks a method of identifyingwhich surveys have the greatest weight in a given decision, thenRespondents must assume that all surveys either have equal or unknownweight, with the result that they will be required to answer each ofthem, regardless of what their actual weight is. If this is the casethen no matter how many surveys are present in a given decision eithereach one of them must be answered or they must take the risk that thesurveys which remain unanswered might be important and may disqualifythem, or the Counterparties they represent, from being selected in adecision.

By example, and not by way of limitation, if no genetic optimizationmodule was provided by the decision support system, and there were 3,000Respondents and 100 surveys, of which only 10 were highly relevant, thenthe system would administer more than 300,000 surveys (consuming aconsiderable number of computational cycles) and it would likewise storeat least 300,000 records to a storage medium (consuming considerablestorage resources). Subsequent queries made to that storage medium wouldlikewise consume considerable computational cycles as each record wouldhave to be compared to the others in various database procedures.

In contrast, if the genetic optimization is provided, as is provided bySystem 1000, then a number of needless computer cycles can be avoided.For instance if each Respondent recognizes that only 10 of the surveysout of 100 are highly relevant then they will complete only the minimumnumber of most relevant surveys. Therefore the decision support systemwill administer only 30,000 surveys and will collect something near thatmany records, to be stored on a storage medium, which is a considerableefficiency over the prior result. All of the downstream computerprocesses will likewise be more efficient by processing fewer recordsand consuming fewer resources. However, this efficiency is available ifthe genetic optimization module and the survey relevance module are bothapplied to the Survey Management Module, FIG. 1 [1030].

3: Define Actions & Criteria

With reference again to FIG. 2, at [step 30] each Party defines a seriesof actions to be performed to other entities meeting a series ofthreshold conditions, a series of actions which are allowed to beperformed to the Party if another entity meets a series of thresholdconditions, and a series of threshold conditions which defines each.With reference to FIG. 3A at [step 101] the System 1000 obtains fromeach entity a series of actions the entity wishes to perform and whichmay be performed to the entity if a series of threshold conditions aremet. These criteria are then stored.

A variety of actions can be performed or be allowed to be performedbased on these threshold conditions. Examples of these include but arenot limited to entities performing the following actions: sharinginformation, sending a message, providing an offer, executing acontract, providing a counter-offer, or accepting an offer.

From each Party a series of actions is defined and obtained whereby anEntity identifies a series of actions they would like to perform if athreshold condition is reached. The threshold condition relates to (i)the presence of one or more attribute levels associated with an Entity,(ii) the presence of a given arrangement of attribute levels associatedwith an Entity, (iii) a utility function evaluation that cumulativelyexceeds a given threshold for a given arrangement, or (iv) the mutualutility function evaluation cumulatively exceeds a threshold valuebetween or amongst Entities for a given arrangement.

In the first case, for example, an Entity might define a certainattribute level which will trigger an action, whereby if one or more ofthose individual attribute levels are met a series of actions aretriggered. For instance, in an employment decision a certain highlysought-after skill may be extremely rare and valuable within anemployment market therefore a potential employer might want tocommunicate immediately with any potential employee that possesses thisskill rather than undertaking the more lengthy evaluation procedure.

In the second case, for example, an Entity might define a certaincombination of attribute levels which might trigger an action, wherebyif a certain series of arrangement of conditions are met (a series ofAND conditions) or a certain combination of sets of conditions are met(a series of OR conditions) the action would be employed by the system.For instance, in a real estate decision a Party acting as a potentialbuyer might want to perform the action of making an offer on any homethat falls within a given area which has been identified AND which has aparticular square foot requirement OR which is valued by a certifiedappraiser above a certain threshold.

In the third case, for example, an Entity might define the conditions ofan action on the basis of a cumulative utility function evaluation,whereby if any counterpart Entity in a given decision meets or exceeds acertain utility value a certain action is performed. For instance, inthe case of a contracting decision a contractor has received the surveyresponses of all subcontractors and has previously narrowed the pool ofpossible candidates for this particular decision through a bidding andoffer process. The contractor therefore wants to define, amongst amatrix of competing utility criteria a certain threshold that must beexceeded. Once this utility value is exceeded the offer will immediatelybe accepted. In such a case the contractor's aggregate utility functionevaluation of the various subcontractor's attribute levels is used asthe threshold criteria for performing an action.

In the forth case, for example, an Entity might define the conditions ofan action on the basis of a cumulative mutual function evaluation whichmust exist between or amongst the various arrangement of Entities inorder for an action to occur. For instance, an investment broker mighthave details of various investments which are confidential but whichthey would be willing to share if the mutual “fit” between theinvestment, the investment broker, and the investor were above a certainthreshold. In such a case the investor would allow the confidentialinformation to be shared if the mutual evaluation between/amongst allentities were above a given threshold.

4: Collect Survey Responses

With reference to FIG. 2 [step 40] and FIG. 3A [step 102],Counterparties and Co-Respondents provide responses to the surveysselected by Parties. In general any completed response of a Respondent,which is indicated by A (

(Q_(e) _(s) )) or (

) in an abbreviated form, can have a variety of attribute levels whichcharacterize it. In the figure each column represents a Responder. Itshould be noted that an array of these attributes is also represented asthe array {

}.

In the example of an employment decision a series of attributes levelsand attributes which might represent a potential employer might be asfollows:

TABLE 1 ATTRIBUTES ATTRIBUTE LEVELS Salary 0, . . . , 30000, 31000,32000, 33000, Medical Benefits none, partial low, partial med, partialhigh, full, full plus Work Environment formal, professional, businesscasual, casual, very casual Typical Career Advancement none, 1 year, 2years, 3 years, . . . , 10+ years Colleague/Co-Worker Interaction none,very low, low, med, high, very high, continual Average Previous EmployeeRating 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Work-Work-Life Balance 0, 1, 2, 3,4, 5, 6, 7, 8, 9 Industry Ranking 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Year OverYear Revenue Growth −1000%, . . . , 1000+% Average Employee Turnover 0,1, 2, 3, 4, 5, 6, 7, 8, 9 Average Previous Employee Rating 0, 1, 2, 3,4, 5, 6, 7, 8, 9 Employee Communication History 0%, . . . , 100% HRIssue Responsiveness History 0%, . . . , 100% Organizational Rating 0,1, 2, 3, 4, 5, 6, 7, 8, 9 Department Staff Size 0, . . . , 10, 11, 12, .. . , ∞ Number Of Supervisors 0, . . . , 10, 11, 12, . . . , ∞ Number OfSubordinates 0, . . . , 10, 11, 12, . . . , ∞ Employee Rating Of CareerTraining Program 0, . . . , 10, 11, 12, . . . , ∞ Employee Rating OfClarity Of Expectations 0, . . . , 10, 11, 12, . . . , ∞ Employee RatingOf Congeniality 0, . . . , 10, 11, 12, . . . , ∞ OrganizationalReporting Structure heavyweight team, lightweight team, matrix,functional Current Annual Business Volume 0, . . . , 50000, 75000,100000, 125000, . . . , ∞ % Time Spent In Traveling 0%, . . . , 100%Institution Credit Score 0, . . . , 100 Audit Opinion Historyunqualified, qualified, disclaimer, adverse Lowest 6 Month CreditBalance 0, . . . , 500000, 750000, 1000000, 1250000, . . . , ∞ Highest 6Month Credit Balance 0, . . . , 500000, 750000, 1000000, 1250000, . . ., ∞ Businesses Scoring Worse 0%, . . . , 100% Bankruptcies 0, . . . ,10+ Judgments Filed 0, . . . , 50+ Collections 0, . . . , 50+For the same employment decision a brief sample of the attributes andattribute levels which might represent a potential employee might be asfollows:

TABLE 2 ATTRIBUTES ATTRIBUTE LEVELS Highest Education Level basic,secondary, associates, bachelors, masters, PhD, multi PhD Number OfIndustry Connections 0, . . . , 70, 75, 80, 85, 90, . . . , 1000+Honesty 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Conscientiousness 0, 1, 2, 3, 4, 5,6, 7, 8, 9 General Ability 50, . . . , 100, 105, 110, . . . , 200Adaptability 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Social Ability 0, 1, 2, 3, 4,5, 6, 7, 8, 9 Employees Managed 0, . . . , 5, 6, 7, . . . , 100+ BudgetsManaged 0, 25000, 50000, 75000, . . . , 10000000+ Requested SkillExperience 1 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Requested Skill Experience 20, 1, 2, 3, 4, 5, 6, 7, 8, 9 Requested Skill Experience 3 0, 1, 2, 3, 4,5, 6, 7, 8, 9 Requested Skill Experience 4 0, 1, 2, 3, 4, 5, 6, 7, 8, 9Requested Skill Experience 5 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 RequestedSkill Experience (n) 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Availabilityavailable, unavailableIn the example of a service decision a brief sample of the attributesand attribute levels which might represent a service provider might beas follows:

TABLE 3 ATTRIBUTES ATTRIBUTE LEVELS Clear Communication History 0%, . .. , 100% Client Responsiveness History 0%, . . . , 100% Safety AuditHistory low risk:, 0%, . . . , 100%, med risk:, 0%, . . . , 100% highrisk:, 0%, . . . , 100% Payment Terms cash, 0%, . . . , 100% net 10, 0%,. . . , 100% . . . net 360 Included Service Level Per Year maintenancevisits:, 0, 1, 2, 3, . . . , 40 training visits: 0, 1, 2, 3, . . . , 40emergency visits: 0, 1, 2, 3, . . . , 40 on-on-call training: 0, 1, 2,3, . . . , 40 On-Time Delivery History 0%, . . . , 100% CoordinationWith Other Vendor History 0%, . . . , 100% Staffing Sufficiency Opinion0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Quality Work Product Opinion 0, 1, 2, 3, 4,5, 6, 7, 8, 9 Legal Compliance Opinion 0, 1, 2, 3, 4, 5, 6, 7, 8, 9Professional Service Rating 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Current StaffSize 0, . . . , 10, 11, 12, . . . , ∞ Current Annual Business Volume 0,. . . , 50000, 75000, 100000, 125000, . . . , ∞ Current Annual BusinessVolume Capacity 0, . . . , 50000, 75000, 100000, 125000, . . . , ∞Current Excess Capacity 0, . . . , 50000, 75000, 100000, 125000, . . . ,∞ General Liability Insurance Coverage 0, . . . , 500000, 750000,1000000, . . . , 100000000 (Technicalexpertise1) 0, 1, 2, 3, 4, 5, 6, 7,8, 9 (Technicalexpertise2) 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 (TechnicalExpertise(n)) 0, 1, 2, 3, 4, 5, 6, 7, 8, 9In the same example of a service decision a brief sample of theattributes and attribute levels which might represent an institutionmight be as follows:

TABLE 4 ATTRIBUTES ATTRIBUTE LEVELS Clear Communication History 0%, . .. , 100% Institution Credit Score 0, . . . , 100 Audit Opinion Historyunqualified, qualified, disclaimer, adverse Lowest 6 Month CreditBalance 0, . . . , 500000, 750000, 1000000, 1250000, . . . , ∞ Highest 6Month Credit Balance 0, . . . , 500000, 750000, 1000000, 1250000, . . ., ∞ Businesses Scoring Worse 0%, . . . , 100% Bankruptcies 0, . . . ,10+ Judgments Filed 0, . . . , 50+ Collections 0, . . . , 50+ AnnualRevenues 0, . . . , 1000000, 1100000, 1200000, . . . , ∞ PaymentSchedule cash, 15, 30, 45, 60, 90, 120,180, 360 Available Work 0, . . ., 50000, 75000, 100000, 125000, . . . , ∞ Work Growth Rate 0%, . . . ,100%, . . . , ∞ (Work Requirement 1) (description1), (description2),(description3), . . . , (description(m)) (Work Requirement 2)(description1), (description2), (description3), . . . , (description(m))(Work Requirement 3) (description1), (description2), (description3), . .. , (description(m)) (Work Requirement (n)) (description1),(description2), (description3), . . . , (description(m))In the example of a real estate decision a brief sample of theattributes and attribute levels which might represent an owner/lessormight be as follows:

TABLE 5 ATTRIBUTES ATTRIBUTE LEVELS Price Per Square Foot 0, . . . ,0.25, 0.26, 0.27, . . . , 7.0, 7.1, 7.2, . . . , ∞ Price Term day,month, quarter, biannual, annual Parking Spaces 0, . . . , 5, 6, 7, 8, .. . , 50, 51, 52, . . . , 1000+ Contract Type fee simple, percentagelease, net lease, double net lease, triple net lease, gross lease SpaceType office, flex, mixed-use, retail, heavy industrial, lightindustrial, residential Utilities Included heating: 0%, . . . , 100%water & sewer: 0%, . . . , 100% electricity: 0%, . . . , 100% telephone:0%, . . . , 100% internet: 0%, . . . , 100% Common Area security: 0%, .. . , 100% Maintenance Included advertising: 0%, . . . , 100% parking:0%, . . . , 100% repairs and renovations: 0%, . . . , 100% taxes &permits: 0%, . . . , 100% Lease Length In Months n/a, 0, 25, 0, 5, 1, 3,6, 9, . . . , 120+In the same example of a real estate decision a brief sample of theattributes and attribute levels which might represent a buyer/lesseemight be as follows:

TABLE 6 ATTRIBUTES ATTRIBUTE LEVELS Credit Available 0, . . . , 5000000,600000, 7000000, 8000000, . . . , ∞ Lease History 0, 1, 2, 3, 4, 5, 6,7, 8, 9 Payment History 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Credit Score 0, . .. , 100 Lowest 6 Month 0, . . . , 500000, 750000, 1000000, 1250000, . .. , ∞ Credit Balance Highest 6 Month 0, . . . , 500000, 750000, 1000000,1250000, . . . , ∞ Credit Balance % Scoring Worse 0%, . . . , 100%Bankruptcies 0, . . . , 10+ Judgments Filed 0, . . . , 50+ Collections0, . . . , 50+ Annual Revenues 0, . . . , 100000, 110000, 120000, . . ., ∞ Free Cash Flow 0, . . . , 10000, 11000, 12000, . . . , ∞In the example of an industrial equipment decision a brief sample of theattributes and attribute levels which might represent an industrialequipment provider might be as follows:

TABLE 7 ATTRIBUTES ATTRIBUTE LEVELS Price Per 0, . . . , 500000, 750000,1000000, 1250000, . . . , ∞ Price Term purchase, hour, day, month,quarter, half-year, year, 2-years Working Hours On Machine 0, . . . ,6000, 7000, 8000, . . . , ∞ Major Maintenance Narrative (event1:hours),(event2:hours), (event3:hours), . . . , (event(m):hours), Peak DailyOutput 0, . . . , 1000, 1100, 1200, . . . , ∞ Average Daily Output 0, .. . , 800, 900, 1000, . . . , ∞, Daily Operational Time In Hours 0, . .. , 8, 9, 10, 12, 13, . . . , 24 Daily Operational Maintenance 0, . . ., 0, 25, 0, 5, 0, 75, 1, 0, . . . , 24 Daily Operators Needed 0, . . . ,1, 2, 3, 4, 5, . . . , ∞ Input interface direct manipulation, graphical,web, touchscreen, command line, file input Physical Dimensions length:1, . . . , 100 width: 1, . . . , 100 depth: 1, . . . , 100 Power Inputsvolts: 1, . . . , 100 amps: 1, . . . , 100 (Equipment Specification 1)(description1), (description2), (description3), . . . ,(description(m)), (Equipment Specification 2) (description1),(description2), (description3), . . . , (description(m)), (EquipmentSpecification 3) (description1), (description2), (description3), . . . ,(description(m)), (Equipment Specification (n)) (description1),(description2), (description3), . . . , (description(m)), IncludedService Level Per Year regular maintenance visits: 0, 1, 2, 3, . . . ,40 emergency maintenance visits: 0, 1, 2, 3, . . . , 40 training visits:0, 1, 2, 3, . . . , 40 on-call-training: 0, 1, 2, 3, . . . , 40 IncludedMaintenance none, parts, labor, visits Shipment Options FOB, C&F, OF, BWInstallation Options none, included, required, partial, deliveryIn the same example of an industrial equipment decision a brief sampleof the attributes and attribute levels which might represent anindustrial equipment user might be as follows:

TABLE 8 ATTRIBUTES ATTRIBUTE LEVELS Institution Credit Score 0, . . . ,1000 Audit Opinion History unqualified, qualified, disclaimer, adverseLowest 6 Month Credit Balance 0, . . . , 500000, 750000, 1000000,1250000, . . . , ∞ Highest 6 Month Credit Balance 0, . . . , 500000,750000, 1000000, 1250000, . . . , ∞ Businesses Scoring Worse 0%, . . . ,100% Bankruptcies 0, . . . , 10+ Judgments Filed 0, . . . , 50+Collections 0, . . . , 50+ Contract Length In Months 0, . . . , 8, 9,10, 12, 13, . . . , 120 Dimensions Of Available Space length:, 1, . . ., 100 width:, 1, . . . , 100 depth:, 1, . . . , 100 Payment Schedule InDays cash, 15, 30, 45, 60, 90, 120, 180, 360 Min Unit Volume 0, . . . ,360000, 396000, 432000, Max Unit Volume 0, . . . , 360000, 396000,432000, . . . , ∞ (Equipment Specification 1) (description1),(description2), (description3), . . . , (description(m)) (EquipmentSpecification 2) (description1), (description2), (description3), . . . ,(description(m)) (Equipment Specification 3) (description1),(description2), (description3), . . . , (description(m)) (EquipmentSpecification(n)) (description1), (description2), (description3), . . ., (description(m))In the example of a financial investment decision a brief sample of theattributes and attribute levels which might represent a financialservices provider might be as follows:

TABLE 9 ATTRIBUTES ATTRIBUTE LEVELS Management Certification CFA, CMA,CGFM, CFRM Professional Investment Opinion 0, 1, 2, 3, 4, 5, 6, 7, 8, 9Historical Return Beta −3, . . . −2, . . . −1, . . . 0, . . . 1, . . .2, . . . 3 Historical Return Statistical Location Parameter −10, 0, −9,9, −9, 8, . . . , 0, . . . , 8, 9, 9, 10 Historical Return StatisticalDispersion Parameter 0, 10, 20, 30, . . . , ∞ Historical Return SkewParameter −10, 0, −9, 9, −9, 8, . . . , 0, . . . , 8, 9, 9, 10Historical Return Kurtosis Parameter −10, 0, −9, 9, −9, 8, . . . , 0, .. . , 8, 9, 9, 10 Exchange Listing n/a, private, NASDAQ, NYSE, CME, ISE,NYMEX, DAX, TSE, LSE, HKSE, TSE, SIX, MICEX, JSE, NSEI, BSE, BME, SSE,ASE, BMF Investment Capitalization 0, . . . , 500000, 750000, 1000000,1250000, . . . , ∞ Number Of Investors 1, 10, 20, 30, . . . , ∞ NumberOf Shares 1, 10, 20, 30, . . . , ∞ Average Investor Statistics(description1), (description2), (description3), . . . , (description(m))Investment Duration Days 1, 7, 14, 21, 30, 60, 90, 120, 180, . . . ,2700, 3200, 3650 Industrial Sector energy, materials, industrials,consumer discretionary, consumer staples, healthcare, financials,info-tech, telecom, utilities Quick Ratio 5, 1, 0, 1, 5, 2, 0, . . . ,500+ Current Ratio 5, 1, 0, 1, 5, 2, 0, . . . , 500+ Cash Ratio 5, 1, 0,1, 5, 2, 0, . . . , 500+ Total Operating Profit Margin 0%, . . . , 100%,. . . , 10000% Total Return On Assets 0%, . . . , 100%, . . . , 10000%Total Return On Equity 0%, . . . , 100%, . . . , 10000% Growth Rate 0%,. . . , 100%, . . . , 10000% Turnover Ratio 0%, . . . , 100%, . . . ,10000% Investment Expenses 0%, . . . , 100%, . . . , 10000% MinimumInvestment Amount 0, . . . , 5000, 7500, 10000, 15000, . . . , ∞In the same example of an financial investment decision a brief sampleof the attributes and attribute levels which might represent an investormight be as follows:

TABLE 10 ATTRIBUTES ATTRIBUTE LEVELS Annual Salary Income 0, . . . ,100000, 110000, 120000, . . . , ∞ Annual Investment 0, . . . , 100000,110000, 120000, . . . , Income Income Stability very unstable, unstable,moderately stable, stable, very stable Cash Equivalent Holdings 0, . . ., 100000, 110000, 120000, . . . , ∞ Net Real Estate Holdings 0, . . . ,100000, 110000, 120000, . . . , ∞ Stock Holdings 0, . . . , 100000,110000, 120000, . . . , ∞ Bond Holdings 0, . . . , 100000, 110000,120000, . . . , ∞ Investment Experience angel funding: 0, . . . , 9private venture: 0, . . . , 9 small-cap stocks: 0, . . . , 9 mid-capstocks: 0 . . . , 9 large-cap stocks: 0, . . . , 9 foreign stocks: 0, .. . , 9 mutual funds: 0, . . . , 9 hedge funds: 0, . . . , 9 privatebonds: 0, . . . , 9 corporate bonds: 0, . . . , 9 government bonds: 0, .. . , 9 foreign bonds: 0, . . . , 9 options: 0, . . . , 9 commodities:0, . . . , 9 foreign exchange: 0, . . . , 9 Loss Aversion 0, 1, 2, 3, 4,5, 6, 7, 8, 9 Liquidity Preference 0, 1, 2, 3, 4, 5, 6, 7, 8, 9Suggestibility 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Investment Time angelfunding: 0, . . . , 120 Horizons(Months) private venture: 0, . . . , 120small-cap stocks: 0, . . . , 120 mid-cap stocks: 0, . . . , 120large-cap stocks: 0, . . . , 120 foreign stocks: 0, . . . , 120 mutualfunds: 0, . . . , 120 hedge funds: 0, . . . , 120 private bonds: 0, . .. , 120 corporate bonds: 0, . . . , 120 government bonds: 0, . . . , 120foreign bonds: 0, . . . , 120 options: 0, . . . , 120 commodities: 0, .. . , 120 foreign exchange: 0, . . . , 120 Investor Risk Profileconservative, moderately conservative, moderate, moderately aggressive,aggressive

The variable (Â) identifies that the variable is a response of aCounterparty or Co-Respondent, the subscript ({acute over (ê)})identifies the value of the Counterparty and Co-Respondent, thesub-subscript (m) identifies the value of the entity which generated thesurvey and (n) identifies the survey which among a number of surveys wasgenerated by the entity.

The process of each Counterparty responding to each survey is alsomathematically represented as:

$\begin{matrix}{{{\hat{A}}_{\overset{\overset{\prime}{\hat{}}}{e}}\left( Q_{e_{s}} \right)}:={\overset{i,j}{\underset{{({e,s})} = 1}{\hat{A}}}\left( Q_{e_{s}} \right)}} & \lbrack 4.01\rbrack\end{matrix}$

The System 1000 communicates with Respondents in order for Respondentsto complete Survey Responses. FIG. 9 outlines the procedure whereby theSystem 1000 communicates with respondents in order to collect SurveyResponses.

The System 1000 attempts to communicate with the Respondent usinginformation provided by either Counterparty from FIG. 5 [270] or Partyfrom FIG. 5 [265]. The System 1000 then identifies whether it cancommunicate with the Respondent ([step 410]). This communication can beperformed in several ways. In the most preferred embodiment thiscommunication occurs through an electronic communication network wherebyan electronic message is sent to the Respondent and the Respondentprovides the Survey Response through the same electronic communicationmedium. Alternatively the contact can be made and the survey conductedbetween two people over a telephone communication network. It can alsobe performed through a letter which requests an individual complete apaper survey and sends the survey to processing facility which processesthe information and returns the results via a communication network. Thesurvey can also be completed by a personal interview whereby theRespondent is interviewed by an interviewer and the interviewer providesthe information to the system over a communication network. If theRespondent cannot be reached then the survey remains incomplete and thereferring entity is informed that the Survey is incomplete [step 480].

Parties are able to identify confidential information that isselectively retrievable only to entities which meet a certain thresholdcriteria. At [step 420] the system identifies whether the surveyinformation is confidential. At [step 430] the system identifies whethersurvey respondents meet the criteria necessary to receive theconfidential materials. At [step 440] Counterparties are presented withall surveys that have been selected by all relevant Parties for a givendecision category or subcategory.

In many situations where a mutual decision is made between more than onecounterpart Parties all users have to duplicate the work of completingeach other's surveys. This includes answering multiple surveys multipletimes, resulting in enormous duplication, in spite of the fact that theinformation requested from many of these surveys and produced inresponse to these many surveys is largely the same duplicatedinformation. Additionally, this duplication results in the use ofconsiderable system resources needed to process each of these tasks, forexample each survey that is administered requires both computationalcycles to administer. Similarly, each response which is gathered from asurvey administration creates a record which requires storage space.Together these both greatly affect all of the downstream dependentsystem processes which require resources which are dependent onidentifying and evaluating response information which has been stored.For instance there is the example of contracting decision, wheresubcontractors and contractors participate in a decision where eachcounterpart Party evaluates and selects its counterpart Counterparties,although the same problem can apply to any multilateral decision.

In the present state of the art, each individual contractor involved ina decision provides a request for a proposal (RFP), a request for bid(RFB), a request for tender (RFT), or a request for information (RFI)from relevant subcontractors which contains a number of surveys. EachCounterparty subcontractor receives the RFPs, RFBs, RFTs, or RFIs andprepares a series of responses which is then sent to each Partycontractor.

In aggregate the number of responses which must be prepared by eachsubcontractor is roughly equivalent to the sum of the number of surveyswhich each contractor individually provides to each subcontractor.Consequently for each subcontractor the number of contractor surveyswhich must be completed is as follows:

$\begin{matrix}{\overset{i,j}{\sum\limits_{{({e,s})} = 1}}Q_{e_{s}}\text{:}\left\{ {x = 1} \right.} & \lbrack 4.02\rbrack\end{matrix}$

Where (Q_(e) _(s) ) are the number of total surveys that eachsubcontractor must complete and (x)=1 represents whether a request for aparticular survey is true and (x)=0 represents whether a request for aparticular survey is false (see [Table 11] for visualization). Most ofthese surveys are duplicates and the responses will be duplicates aswell. Because each subcontractor has to complete each RFPs, RFBs, RFTs,or RFIs to be considered in the decision, all the subcontractorscollectively complete:

$\begin{matrix}{\left( {\overset{i,j}{\sum\limits_{{({e,s})} = 1}}{Q_{e_{s}}:\left\{ {x = 1} \right.}} \right)k} & \lbrack 4.03\rbrack\end{matrix}$

Where (k) is the number of subcontractors in this case. Consider thateach contractor must also likewise meet the decision criteria of eachcounterpart subcontractor through either a formal or informal surveyingprocess. The number of surveys which must be responded to growscombinatorially to the following evaluation:

$\begin{matrix}\left( {{\sum\limits_{{({e,s})} = 1}^{i,j}{Q_{e_{s}}\text{:}\mspace{11mu} \left\{ {x = 1} \right)k}} + \left( {\sum\limits_{{({e,s})} = 1}^{g,h}{Q_{e_{s}}\text{:}\mspace{11mu} \left\{ {x = 1} \right)\mspace{11mu} l}} \right.} \right. & \lbrack 4.04\rbrack\end{matrix}$

Where (l) represents the number of contractors. It is then no wonderthen why most decisions generally only involve a small set or subset ofcounterpart Entities engaged in a mutual decision. For if each of theseresponses represented even a small quantity of finite time and effortwhich must be performed then a large combinatorial set would quicklyoverwhelm any Party or Counterparty with a sufficiently large number. Ifwe were to consider multiple sets of role relations where a decision hasmore than one counterpart role the situation becomes even moreoverwhelming. This same situation likewise applies with relation to thesystem resources which must be utilized to efficiently process decisioninformation. A large set of Parties or Counterparties quickly sums to alarger number of system processes and records than can be reasonablyadministered or recorded by a system.

Consider the simple case where the number of subcontractors involved ina decision is 50 and the number of contractors involved in a decision is20 and number of surveys which subcontractors must respond to is onaverage 30 and the number of surveys which contractors must respond ison average 15. In this case the number of subcontractor responses wouldhave to be equal to or greater than (50*20*30=30,000) and the number ofcontractor responses would have to be equal to or greater than(20*50*15=15,000) totaling equal to or greater than 45,000 responses.The sheer number volume of responses limits the capacity of any oneindividual, or group of individuals, from reasonably evaluating morethan a few potential Party/Counterparty combinations if there is even anegligible resource cost for each response.

Accordingly, System 1000 provides a means to minimize the number ofmutual surveys which each party requests the other complete byaggregating all of the relevant surveys which apply to each decision andeach role and presenting to each Respondent only the union set of uniquesurveys which are to be completed by the Respondent. The Respondentprovides a survey response to each survey and the response provided isutilized in all cases where the Party requested a survey response fromeach Entity.

This procedure reduces the number of surveys which have to be completedby one whole factor in each case. It furthermore reduces the number ofsystem survey administrations and records by the same number. In theabove example this would indicate that the total number of surveys whicheach subcontractor entity would have to complete (and which would beadministered and recorded by the system) would be approximately only 30surveys-totaling only 600 for all subcontractors, and each contractingentity would have to complete approximately only 15 surveys-totaling 750for all contractors.

[Table 11] shows this concept by reducing the number of survey responseswhich each Party requests each Counterparty to complete. The concept isillustrated visually, where the reduction by the union set of surveysreduces the total number of surveys which the Respondent has to complete(and which the system has to administer and record) from 95 to 7-(thetotal number of surveys as represented by the columns) resulting insignificant labor savings. Within the table [Table 11] the identifiedone (1) values represent instances where entity (en) has requested aresponse to survey (s), in contrast the zero (0) values representinstances where the survey is not requested to be completed by a Party.The one (1) and zero (0) values correspond to the (x) value in formulae[4.02, 4.03, 4.04].

TABLE 11 Survey Responses Required S₁ S₂ S₃ S₄ S₅ S₆ S₇ e₁  1 0 0 0 0 01 e₂  1 0 0 1 1 0 0 e₃  0 0 1 0 1 0 0 e₄  0 1 0 0 0 1 1 e₅  1 1 1 1 0 11 e₆  0 1 1 0 0 1 1 e₇  0 0 0 0 0 1 1 e₈  1 0 1 1 0 1 1 e₉  0 1 1 0 1 00 e₁₀ 0 0 0 0 1 0 1 e₁₁ 0 1 1 0 0 1 0 e₁₂ 1 1 1 1 0 1 0 e₁₃ 1 1 0 0 1 11 e₁₄ 1 1 1 1 1 0 0 e₁₅ 1 0 1 0 0 1 1 e₁₆ 1 1 1 1 0 1 0 e₁₇ 1 1 0 1 1 01 e₁₈ 0 0 0 1 0 1 1 e₁₉ 1 0 0 1 1 0 1 e₂₀ 1 1 0 1 1 1 0 e₂₁ 0 1 1 1 0 00 e₂₂ 0 0 1 0 1 0 1 e₂₃ 1 1 1 1 0 0 1 e₂₄ 1 0 1 1 0 1 0 e₂₅ 0 1 1 0 1 10 total = 95 individual surveys${\bigcup\limits_{e = 1}^{i}Q_{e_{s}}} = {7\mspace{14mu} {surveys}}$

This is done so for the purpose of minimizing the total time which isrequired for Counterparties to complete surveys (as well as for thesystem resources which are required to administer surveys and recordresponses to a medium)—since in many cases Parties request the sameinformation from Counterparties-which means that a single response toone Party survey may also meet the criteria of many other Party surveys.Counterparties then select the surveys they will complete or haveCo-Respondents complete either in conjunction with or on their behalf.

The purpose of the surveying design is to encourage a relevant standardset of surveys to be utilized in each category of information query thateach Party will collect from the corresponding entity without the needfor an explicitly designed array of surveys. Such an array of surveysrequires an administrator of the system to define and design thespecific survey arrangement that meets the criteria of all partiessimultaneously. In contrast, the system and methodology of the presentinvention therefore employ an asymmetrical survey design for threereasons: (i) because asymmetrical surveys better reflect most real lifesituations where Parties interests do not simultaneously reflect thosecounterpart Parties, (ii) because the ability to not have anadministrator allows Parties to develop new categories of decisions andcriteria which an administrator might not be able to conceive and (iii)the system design allows for cross-categorical survey comparisonsallowing for the greatest possible arrangement of options for eachcounterpart in a decision. Such an increase in cross-categoricalcomparisons allows for a greater multiplicity of decision arrangementswhich increases aggregate utility for all decision counterparts.

It should be noted surveys may be presented to Counterparties andCo-Respondents in a hierarchical fashion in order to improve the contextof the data provided to the system. As such a taxonomical tree ofcategories is provided. Each branch of the taxonomical tree is acategory. An example of a hierarchical taxonomy or tree is presented inFIG. 8. This figure illustrates that a survey taxonomy may be presentedin an (n) number of subordinate branches.

Surveys can take a broad array of forms. Surveys may be graphical,auditory, written, audio-visual, numerical, programmatic, or any varietyof media which can be processed through a computer. Respondents selectwhich ones to complete as identified in FIG. 6 steps [305], [310] and[315].

In a preferred embodiment, functionality is included wherebyCo-Responders provide additional information, or co-responses, relatedto a Counterparty Entity. These co-responses may take one of severalforms: (i) this information may be provided in the form of theverification of one or more Counterparty survey responses to identifywhether the response provided by the Counterparty is accurate or not;(ii) the information provided by a Co-Respondent may be in the form of asurvey response on behalf of a Counterparty or (iii) a Co-Respondent mayprovide a separate co-response directly to surveys which are posed fromthe Party to the Co-Respondent about the Counterparty entity. Suchsurvey responses may be an assessment of the objective or subjectiveattributes which a Counterparty may or may not possess which are knownor perceived by the Co-Respondent. These were previously identified in[step 255] and [step 260] of FIG. 5.

With reference to FIG. 9, at [step 450] Survey Responses are provided byCounterparties and Co-Respondents in quantitative, qualitative, andcategorical responses to surveys which can be evaluated by a computingdevice. (It should be noted that computing devices can process andevaluate numerical, textual, pictographic, binary, programmatic,sequential, digital, and analog signal responses. Each of these formatscan serve as an appropriate response for a given Party depending on theprecise needs or interests of the Party.)

At [step 460] a test is performed to identify whether the SurveyResponses meet the completion criteria which the Party defined in [step225] of FIG. 6. If the Survey Response does not meet these criteria itis rejected. At [step 465] if the response was rejected then the systemdetermines whether the Respondent was a Co-Respondent or whether it wasa Counterparty. At [step 480] if the Entity assigned the Survey Responsewas a Co-Respondent then a message is sent by the referring Entity toinform them of the failure. As indicated in [step 490] the SurveyResponse remains incomplete. If the Entity assigned the Survey Responsewas not a Co-Respondent then [step 490] the Survey Response remainsincomplete and the system proceeds to the next step [499].

If the test at [step 460] determined that the Survey Response met thecompletion criteria then the system proceeds to store the SurveyResponses along with the audit and verification information. The type ofaudit and verification information records a timestamp, thecommunication network information which identifies the Respondent alongwith any self-verifying surveys which help to identify the Respondent,and the relationship they have with the Counterparty. Afterwards thesystem proceeds to the next succeeding step [499].

5: Get Preference Responses

With reference to FIG. 2 [step 50] and FIG. 3A, [step 103], once asufficient number of responses and co-responses have been provided tothe system a Party undertakes the evaluation of the Survey Responses inorder encode its information necessary for making a decision. The systempresents Evaluators with a series of Survey Responses and DummyResponses designed to force Party and Co-Evaluators to reveal theutility values they place on the following: (i) attribute levels withrespect to other attribute levels within a given attribute, (ii)attributes with respect to other attributes, and (iii) collections ofattributes with respect to other collections of attributes which canalso extend to evaluating entities with respect to other entities.Through this systematic process each Party ranks the survey responses ofCounterparties. The Evaluator may or may not evaluate all SurveyResponses. Once a sufficient number of Survey Responses has beenevaluated relatively similar responses which have been provided byRespondents can also be evaluated by the system without exposing themdirectly to the Evaluator. In this way the system provides a means foran Evaluator to evaluate an unlimited number of Entities and SurveyResponses by performing a limited number of evaluations. In this way thesystem magnifies the ability of an Evaluator to evaluate vastly moreinformation than they could possibly evaluate without the facilitationof the system.

The process associated with each Party evaluating each survey responseis also mathematically represented in the general case as (

) in where the evaluation proceeds to evaluate attribute levels withrespect to other attribute levels within a given attribute attributeswith respect to other attributes as mentioned in the above paragraph in(i) and (ii). This evaluation can also apply to evaluating collectionsof attributes with respect to other collections of attributes.

$\begin{matrix}{{R_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={\underset{{({e,s})} = 1}{\overset{p,q}{Eval}}\left( {\overset{n}{\bigcup\limits_{\overset{\prime}{\hat{e}}}}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)} \right)}} & \lbrack 5.01\rbrack\end{matrix}$

In an alternative general case represented as (

) evaluation proceeds to evaluate entities with respect to otherentities as is indicated in (iii).

$\begin{matrix}{{{\overset{\sim}{R}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={\underset{\overset{\prime}{\hat{e}} = 1}{\overset{n}{Eval}}\left( {\overset{p,q}{\bigcup\limits_{{({e,s})} = 1}}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)} \right)}} & \lbrack 5.02\rbrack\end{matrix}$

Evaluators provide a series of forced-choice responses to a series ofSurvey Responses in order to yield their Preference Responses.Evaluators can provide Preference Responses to either discrete sets ofSurvey Responses along a single attribute dimension or alternativelythey can provide Preference Responses multiple attribute dimensionssimultaneously. The advantage of providing Preference Responses alongmultiple attribute dimensions simultaneously is that it allows thesystem to reduce the total number of comparisons needed in order togenerate a Utility Preference Model for the Party or Co-Evaluator.

The specific method of Parties and Co-Evaluators providing PreferenceResponses to the System 1000 can be performed in a variety of ways;however, in one embodiment of the system, Preference Responses areprovided as evaluations of sets and subsets of Survey Responses in orderincrease the speed of gathering adequate Preference Response datanecessary to generate the Utility Preference Model with the additionalbi-product that it better maintains the monotonicity of that same model.

FIG. 10A provides an illustration of this particular method where therankings or Preference Responses, (

), for each subset of survey responses is provided in a series of stepsthat evaluates subsets of responses for surveys at a given time. Forexample, in FIG. 10A the first Preference Response, identified as (

), evaluates the survey responses of Respondents {Â₂, . . . , Â_(i)} forthe survey responses {Q₁ ₁ , . . . ,Q₂ ₁ }. The second PreferenceResponse, identified as (

), evaluates the survey responses of Respondents {Â₄, . . . , Â_(i)} forthe survey responses {Q₂ ₁ , . . . ,Q₃ ₁ }. The secondary PreferenceResponse evaluates an overlapping portion of the first PreferenceResponse so that the first and second Preference Responses can bestatistically related to one another to ensure monotonicity betweencertain Preference Responses. As can also be seen in FIG. 10A, subsetsof responses evaluated need not include all responses from everyresponder. Any subset of an adequately diverse array of Survey Responsescan provide a suitable arrangement of Survey Responses from which anEvaluator can create a Preference Response.

FIG. 10B identifies a similar ranking scheme as FIG. 10A with thedistinction being that the domain of evaluation is along the collectionsof attributes. This distinction is identified as (

). In either case the domain of evaluation can vary along either the(Q_(e) _(s) ) or the (

) dimension as S

demonstrated in FIG. 10A and FIG. 10B (collectively FIG. 10).

The above can be represented in a functional mathematical design as anextension of the general case indicated as (

) and (

) as follows for each case.

$\begin{matrix}{{R_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={\underset{{({e,s})} = 1}{\overset{p,q}{Eval}}\left( {{\overset{n}{\bigcup\limits_{\overset{\prime}{\hat{e}}}}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:\left\{ \begin{matrix}\left\lbrack {s \in {{{\mathbb{Z}}\text{:}\mspace{11mu} l} \leq s \leq m}} \right\rbrack \\\left\lbrack {e \in {{\mathbb{Z}}:{j \leq e \leq k}}} \right\rbrack\end{matrix} \right)} \right.}} & \lbrack 5.03\rbrack \\{{{\overset{\sim}{R}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={\underset{\overset{\prime}{\hat{e}} = 1}{\overset{n}{Eval}}\left( {{\overset{p,q}{\bigcup\limits_{{({e,s})} = 1}}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:\left\{ \left\lbrack {\overset{\prime}{\hat{e}} \in {{{\mathbb{Z}}\text{:}\mspace{11mu} h} \leq \overset{\prime}{\hat{e}} \leq i}} \right\rbrack \right)} \right.}} & \lbrack 5.04\rbrack\end{matrix}$

The “subject to” notation directly to the right of the primaryfunctional expression indicates that the array of elements is limited ineach case to a particular domain of evaluation which is provided by thesystem like those demonstrated in FIG. 10A in the case of evaluation[5.03] and FIG. 10B in the case of evaluation [5.04].

In all cases the result of the evaluation either of two arrays ofresponses (i) {

} or (ii) {

} respectively which can be evaluated by various forms of regressionanalysis, curve fitting, and other statistical techniques utilized forfunctional discovery which result in the generation of a UtilityPreference Model function which yields a best-fit for the dependentarray identified as the Response Preference from the independent arrayidentified as the Survey Response.

6: Derive Utility Functions

A Utility Preference Model generation function, UPM(•), is used tocreate a utility function,

(•), which represents the generalized choices which an Evaluator wouldmake if provided with a set Survey Responses. The UPM functiongeneration is employed for each individual Party and Co-Evaluator usingthe Preference Responses according to the here identified methods: (i)conjoint analysis, (ii) stated preference analysis, (iii) discretechoice analysis, and (iv) other forced-choice methodologies.

The essence of these methods are that they statistically evaluate thePreference Responses of Evaluators relative to the Survey Responsesprovided by the Respondents in order to statistically approximate afunction which best explains the relationship between the PreferenceResponses and the Survey Responses. The methods employed for thisstatistical approximation include: (i) regression analysis, (ii) curvefitting, (iii) artificial neural network analysis, (iv) polynomialinterpolation, (v) and other statistical techniques utilized forfunctional discovery.

See the following for examples of conjoint analysis and forced-choicetechniques:

-   Cattin, P. and Wittink, R. “Commercial Use of Conjoint Analysis: A    Survey”, 45 Journal of Marketing 44-53 (No. 3, Summer, 1982) and    “Commercial Use of Conjoint Analysis: An Update”, 53 Journal of    Marketing 91-96 (July, 1982)-   Green, P. E. and Wind, Y. “New Way to Measure Consumer's Judgments”,    Harvard Business Review, July 1975-   Green, P., Krieger, A. and Wind, Y. (2001) “Thirty years of conjoint    analysis: reflections and prospects”, Interfaces, Vol. 31, No. 3.-   LoPinto, Frank A. and Ragsdale, Cliff T. (2009). “Efficient modeling    of individual consumer preferences: facilitating agent-based online    markets”, International Journal of Electronic Marketing and    Retailing 66-81, (Volume 3, Number 1/2010).-   Ramirez, Jose Manuel (2009). “Measuring: from Conjoint Analysis to    Integrated Conjoint Experiments”. Journal of Quantitative Methods    for Economics and Business Administration 9: 28-43.

See also references identified in the bibliography of Patrick Bohl:Conjoint Literature Database CLD, University of Mainz, Germany, 1997.

The foregoing articles and references are hereby incorporated herein byreference.

A utility function is created which represents an individual Evaluator.The utility function provides a generalized best-fit predictiveapproximation of the dependent array of Response Preferences which anevaluator would assign as output values from the input values of theindependent array of Survey Responses. Consequently, the utilityfunction serves to approximate the relative value assignments which theEvaluator would have assigned a series of attribute levels if thosevalues had been assigned by a Respondent.

In furtherance of the above, the Utility Preference Model generationfunction is employed to derive two possible utility functions: (i) autility function which provides the Party's utility for each attributelevel with respect to other attribute levels within a given attributecategory, as well as each attribute with respect to other attributes, aswell as each collection of attributes with respect to other collectionsof attributes; or alternatively (ii) a utility function which providesthe Party's utility for each relevant Counterparty Entity.

The first possible utility function, identified in the above as (i) inthe above paragraph is displayed on FIG. 3B, step [105]. The secondpossible utility function, identified as (ii) in the above paragraph isdisplayed on FIG. 3B, step [107]. These utilities are alsomathematically represented herein as follows, which can be representedas the evaluation of a system of functions in the first case or as asystem of arrays in the second case. Both are equivalent:

$\begin{matrix}{{U_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{UPM}\left( {{R_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)},{{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)}} \right.}} & \lbrack 6.01\rbrack \\{{U_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{UPM}\left( {\left\{ R_{e}^{''} \right\},\left( {\hat{A}}_{{\hat{e}}_{e_{s}}}^{\prime} \right\}} \right)}} & \lbrack 6.02\rbrack\end{matrix}$

The Utility Preference Model generation function UPM(•) here representsa statistical procedure used to discover the functional relationshipbetween the Preference Response and the Survey Response as well as thevote aggregation procedure. The utility function derived from theevaluation of Evaluators' Preference Responses for Responders SurveyResponses can be expressed in two ways: (i) as above where the utilityfunction represents the utility that Evaluators have for individualattribute levels of attributes expressed as Survey Responses or (ii) asthe utility which Evaluators have for collections of attribute levels ofattributes, which collections can also serve as proxy for a set orsubset of attributes which represents the Entities themselves.

The distinction between these two methods is nuanced; however, in thesecond case the whole is more than the sum of its parts—meaning that thetotal utility conveyed to a Evaluator would be greater than the sum oftwo or more discrete utilities values alone. In such a case themathematical representation uses the superscript tilde ({tilde over(•)}) in order to distinguish the two methods. This utility function canalso be represented as a system of arrays just as above.

$\begin{matrix}{{{\overset{\sim}{U}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{UPM}\left( {{{\overset{\sim}{R}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)},{{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)}} \right.}} & \lbrack 6.03\rbrack \\{{{\overset{\sim}{U}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{UPM}\left( {\left\{ {\overset{\sim}{R}}_{e}^{''} \right\},\left( {\hat{A}}_{{\hat{e}}_{e_{s}}}^{\prime} \right\}} \right)}} & \lbrack 6.04\rbrack\end{matrix}$

A Party's ranking or evaluation of these Survey Responses may beconducted in conjunction with Co-Evaluators which may either evaluatepart or all of the survey responses provided by Counterparties andCo-Respondents. Functionality is preferably provided for Parties toincorporate the evaluations of other Evaluators within a single utilityfunction through the use of a vote aggregation method (VAM). Theprincipal Party identifies which rules within the vote aggregationmethod apply to integrating the votes within the Party and Co-Evaluatorrelationship to determine how the separate utility functions of theseevaluators integrates to result in the formation of an aggregate utilityfunction. Within the embodiment of this disclosure the rules whichdefine this voting system are designated as (f).

In particular, the application of voting methods within the decisionsupport system reduces the temporal distance between data entry andsystem evaluation because it can apply the encoded group preferences(using vote aggregation procedures) to evaluate each set of attributesthat belong to each Counterparty attribute in a very few computercycles. This contrasts with the prior art, which does not encode grouppreferences using vote aggregation procedures, nor does it then applythese encoded group preferences to evaluate Counterparty attributes.Because of this reduction in temporal distance between data entry andsystem evaluation, the number of computer cycles which occurs betweenthese two events is reduced. This leads System 1000 to achieve a betterutilization rate than the prior art. (The utilization rate here isdefined as the ratio between number of slack-periods and the numbercomputer cycles necessary to complete a process.)

The method for integrating the Preference Responses of Parties andCo-Evaluators is performed by applying a series of voting systemevaluation rules to the utility functions generated by the UtilityPreference Model generation function. All the possible permutations ofthe vote aggregation methods of the voting system are too varied to beindividually described herein; however, all of the vote aggregationmethods employ similar common elements which can be described in detailhere. These common elements are as follows:

-   -   (1) Each vote aggregation method of the voting system identifies        candidates which can be selected or alternatively excluded. In        this case the description of candidates which can be selected or        excluded encompasses various decision options which can be        selected or excluded which also encompasses any selection from        amongst a group of one or more Entities.    -   (2) Each vote aggregation method of the voting system employs a        threshold of selection wherein a certain value or quota must be        obtained in order for a selection to be considered valid or        alternatively invalid in certain cases.    -   (3) The vote aggregation method of the voting system contains a        method of weighting various votes wherein a vote can receive any        weight from negative infinity to positive infinity.    -   (4) The vote aggregation method of the voting system contains a        voting sequence which includes one or more tiers of voting.    -   (5) The vote aggregation method of the voting system contains        various players or actors who are able to choose candidates or        options.    -   (6) The vote aggregation method of the voting system contains        various classifications wherein either players or alternatively        candidates may select or be selected respectively according to        the specific classification into which they fall where there are        one or more classifications.    -   (7) The vote aggregation method of the voting system contains        various conditions wherein candidate information and selections        are made available. Any combination of the above can be employed        by one or more a Parties and Co-Evaluators in the process of        evaluating entities within the scope of this invention.

For the purpose of appropriately interpreting the application of variousvote aggregation methods of the voting system, the following termscommonly used in describing the elements of vote aggregation methods andvoting systems which have or will be used can be transposed orinterchanged so that they might have application within this disclosure.“Candidate” or “options” can be substituted as a Counterparty, Entity,or Respondent depending on the context. “Player” or “actor” can besubstituted as a Party or Evaluator depending on the context. “Quota”can be interpreted as a threshold rule.

Several voting system rules and designs are within the scope of thisinvention. The vote aggregation method of the voting systems include butare not limited to: blackball method, white-ball method, dictator votingmethod, multiple quota method, weighted voting method, plural votingmethod, selective weighted method, multiple class voting method,Condorcet method, Borda method, Copeland method, Nanson method, Dodgsonmethod, Eigenvector method, cardinal utility method, successiveselection method, single transferable vote method, simple majority,runoff method, limited vote method, cumulative vote method, and theBlack method. A more full description of various voting methods whichare applicable is included in the following references. These areincluded within the embodiment of this disclosure.

-   Cranor, Lorrie. “Vote Aggregation Methods”. Declared-Strategy    Voting: An Instrument for Group Decision-Making. Retrieved Mar. 26,    2012 from: http://lorrie.cranor.org/pubs/diss/node4.html.-   Arrow, Kenneth J. (1951, 2^(nd) ed., 1963) Social Choice and    Individual Values. New Haven: Yale University Press. ISBN    0-300-01364-7-   Boix, Cares (1999). “Setting the Rules of the Game: The Choice of    Electoral Systems in Advanced Democracies”. American Political    Science Review 93 (3): 609-624. Doi:10.2307/2585577. JSTOR 2585577.-   Colomer, Josep M., ed. (2004). Handbook of Electoral System Choice.    London and New York: Palgrave Macmillan. ISBN 9781403904546.    Each of the foregoing is hereby incorporated by reference.

FIG. 11 illustrates the steps associated with the process of creatinggroup utility functions. At [step 610] the principal orchestrating Useris provided with a list various vote aggregation rule systems which maybe applied to their evaluation of a decision. From this list they selecta vote aggregation rule system which they would like to apply to aparticular decision. This identified vote aggregation system is storedto an electronically retrievable medium.

At [step 615] depending on the particular roles and rules which existwithin the particular vote aggregation system, the system prompts usersto define the various roles which they would like participating Entitiesto play within their vote aggregation process. This is stored to anelectronically retrievable medium. At [step 620] the system determineswhether sufficient utility functions exist for the vote aggregation tobe immediately applied. If there are sufficient utility functions thenthe creation of a group utility function proceeds [step 670]. If thereare not sufficient utility functions then the system diagnoses thereason for why the utility function cannot be created beginning with[step 625] determining whether all Entities identified indeed exist. At[step 630] if all Entities do not exist, for instance the principal Userhas identified an Entity they would like to participate in the voteaggregation procedure which the system does not recognize, then thesystem at [step 630] prompts the user to provide the contact informationand a contact method in order to communicate with the Entity and createboth the Entity profile and an individual utility function, asidentified in steps [103] and [105] or [107] (FIG. 3). The User thenprovides this information. Next, at [step 635] the system attempts tocontact the individuals according to the contact information and methodsprovided by the referring User. At [step 640] if the system is unable tocontact the Entity then the system sends a message to the referring Userprompting them to select either a different vote aggregation rule or toprovide different contact information or a contact method in order tocontact the individual they have identified to participate in the voteaggregation process [step 610].

If sufficient utility functions exist and Entities exist, thenevaluation proceeds to [step 650] where the Entity being incorporatedinto the evaluation determines whether or not to use an existing utilityfunction for the evaluation or not. At [step 655] if the Entity isdetermines not to use an existing utility function, then the Entityselects whether to create a new utility function or to modify anexisting utility function. If the Entity selects to create a new utilityfunction then they undergo the process of encoding their utilities byproviding Preference Responses to the system for various RespondentSurvey Responses. Otherwise if the Entity has determined to modify autility function then they select one or more elements of the utilityfunction which they would like to modify [step 665]. This can beperformed through two possible means: (i) the User identifies particularSurvey Response categories or equivalently, attribute levels, for whichthe utility function does not serve their particular needs. In thiscase, the system identifies the dependent relational attributes withinthe function which help to describe that particular user's preferencesand provides the user with Survey Responses which address theseparticular attributes collectively in order to determine the relativepreferences of these attributes with respect to the rest of thefunction. The Utility Preference Model generation function then createsa new utility function based on the new Preference Responses provided bythe user. Or, (ii) an expert User may directly modify the functionalcoefficients of the utility function.

At [step 670] If the Entities determine to use either existing utilityfunctions [step 650] or have sufficiently created [step 660] or modified[step 665] utility functions then evaluation proceeds to apply theutility functions through a series of vote aggregation procedures whichcreates a group representative utility function. Each of the possibleutility functions is aggregated through a vote aggregation method asindicated below. The aggregation rules are defined by the term (P) whichboth defines both the rules of vote evaluation and which Evaluatorutility functions participate in the aggregation procedure.

$\begin{matrix}{{U_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{\overset{n}{\underset{\overset{''}{e} = 1}{Aggr}}\left( {{U_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)},{\overset{¨}{r}}_{e}^{''}} \right)}:\left\{ {\overset{''}{e} \in {\overset{¨}{r}}_{e}^{''}} \right.}} & \lbrack 6.05\rbrack \\{{{\overset{\sim}{U}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)}:={{\overset{n}{\underset{\overset{''}{e} = 1}{Aggr}}\left( {{{\overset{\sim}{U}}_{e}^{''}\left( {{\hat{A}}_{\hat{e}}^{\prime}\left( Q_{e_{s}} \right)} \right)},{\overset{¨}{r}}_{e}^{''}} \right)}:\left\{ {\overset{''}{e} \in {\overset{¨}{r}}_{e}^{''}} \right.}} & \lbrack 6.06\rbrack\end{matrix}$

Utility Preference Model generation functions and utility functions arestored to an electronic retrievable media where they can be retrieved,reused and modified to facilitate other decisions determined by theParty. A method is provided for either (i) individual utility functionor (ii) group utility function to be evaluated and modified by selectedUsers. The ability to store and retrieve Utility Preference Modelgeneration functions and utility functions allows for several featureswhich are included within the scope of this invention. It is also withinthe scope of this invention to evaluate the varying differences whichmay the preferences of Parties to the attributes of Counterparties andto be able to identify and evaluate these differences.

Accordingly the means are provided for the following:

-   -   (i) The asynchronous collection of utility preferences by        various Evaluators.    -   (ii) The collection, storage, and re-use of Utility Preference        Model generating functions and utility functions. A means is        also provided that once a series of utility preferences have        been sufficiently accumulated and integrated for the creation of        a utility function for a particular decision that the utility        function which facilitates such decisions can be reused without        the need of additional preference accumulation and integration        in order to facilitate a similar decision.    -   (iii) A means is provided also that a User can selectively        modify one or more parameters that comprise the Utility        Preference Model generating function and the output utility        function in order to optimize one or more goal outcomes which        the Party has specified. This provides that after a utility        function has been created and stored, that it can be afterward        modified in order to either, (a) test various modifications to        the utility function to see if these modifications might lead to        better alternative decision outcomes—in which cases Evaluators        might be able to identify changes which can be made to either        individual preferences or vote aggregation procedures in order        to modify the utility function which might lead to these        modifications, or (b) in order to modify the utility preference        model generating function or the utility functions to update        them without having to undergo the complex evaluation of all        relative attributes.

An example of which is that a Party creates a utility function whichthey used and are highly pleased to re-use frequently; however, a smallbut significant change in the applicability of Counterparty attributeshas occurred such that utility function that previously reflected theParty's preferences now no longer does so. In this case a Party couldsimply update the relative coefficient factors that relate to thatattribute or attribute levels in order to continue to reuse the utilityfunction.

(iv) A means is also provided where a User can selectively modify thevote aggregation method in order to optimize one or more goal outcomeswhich the Party has specified. Similar to modifying the utility functionabove there are several instances where a Party may identify a reason tochange the vote aggregation procedure. These may be (a) to test variousmodifications which could be made to the vote aggregation procedurewhich might lead to better alternative decision outcomes, or (b) inorder to either update the utility function for changes that may haveoccurred within an organization which may alter the way a decision ismade. If such were to occur the utility function needs to be able toreflect these potential changes through the vote aggregation procedure.

(v) Additionally, a means is also provided for Users to identify andmeasure the degree of decision influence which various stakeholders haveon a decision process and how the varying differences in those decisioninputs relate to decision outputs.

(vi) A means is provided for Users to identify and measure the degree ofdifference which a given arrangement of attributes may have relative toanother given single arrangement or set of arrangements for a given setof utility functions.

An example of (vi) in use is the case of an employment decision is thatan potential employer may wish to understand how competitive they arerelative to other potential employers for the interest of potentialemployees. In this case the potential employer could evaluate the scorewhich the attribute arrangement they possess with the utilitypreferences of potential employees and compare that with the attributearrangements of one or multiple other potential employers for the samegroup of potential employees. This would enable the potential employerto be able to identify attributes where they are non-competitive andwhere they might be able to invest greater resources in order to gainthe attention of a better pool of potential employees.

Alternatively, this same feat could be accomplished by potentialemployees who could evaluate the attributes they possess against thoseof other potential employees in what can be effectively termed the“applicant pool” such that they can better understand the features thateither make them competitive or which need improvement in order toinvest their limited resources to become competitive with the rest ofthe market.

Alternatively this same feature could be applied in the case of aservicing decision where service vendors could compare themselves toother vendors relative to the interests of service consumers. Serviceconsumers could compare themselves to other service consumers relativeto the preferences of vendors. This could also be applied similarly inthe case of a real estate decision where an owner or owner's agent couldcompare the attributes which their entity might offer relative to otherentities relative the interests of buyers/leasers and buyers/leaser'sagents or counter-wise. As well, this could be applied in the case of afinancial investment decision where the relative attributes of afinancial investment could be compared to the attributes of otherfinancial investments relative to the interests of investors orcounter-wise.

7: Mutually Evaluate Utility Functions

FIG. 2 [step 70] and FIG. 3B [step 108] calculate and total the utilityfunctions for each Party acting in one role of a decision are applied toeach Survey Response provided by each arrangement of Counterpartiesacting in another role in order to calculate a mutual utility value.This mutual utility value is able to identify the arrangements ofEntities which can provide, ideally, with the highest possible utilityfor all Parties participating in a decision.

The calculation of a mutual utility score for each Party andCounterparty combination within a decision enables System 1000 todiscover how closely aligned each mutual arrangement of Parties,Counterparties, and other decision participants attributes is to each ofthe preferences of the Counterparties, Parties, and other decisionparticipants respectively. This enables the system to very quicklydetermine which Party and Counterparty arrangements are mutually optimalconfigurations in a small number of computer cycles.

This scoring module provides significant technical benefits over theprior art system. The prior art system fails to scorepreference-to-attribute configurations-meaning that a score provided bythat prior art system is unable to deliver reliable results, given thatit is unable to detect the presence or absence of attributes in relationto preferences. Because it is unable to detect the presence or absenceof attributes in relation to preferences the prior art system must yielda higher ratio of false-positives and false-negatives to true-positivesand true-negatives than the asymmetrical multilateral decision supportsystem.

System 1000's result is accomplished only after the utility function foreach Party has been generated and it has been applied to eachCounterparty (steps [106] and [107]). Having the utility functionenables the system to compare attributes or collections of attributes topreferences for as many entities as are part of the system. The systemdoes this by consecutively evaluating all of the attributes or attributecollections which a target Entity has at the utilities of a given Partyand then totaling the sum of these values. This computerized method isdemonstrated mathematically in the following formula. The elementsidentified in the formula are as follows: the input of the formula is(P(

) where (

) represents the Party and (

) represents the Counterparty and the utility functions are taken fromthe above formulas. Naturally the OR operator, (V), is used to indicatea choice of operations depending whether discrete attributes are to beevaluated or whether attribute collections are to be evaluated.

$\begin{matrix}{{\overset{n}{P}\left( {\overset{\overset{n}{''}}{e},\overset{\overset{m}{\prime}}{e}} \right)}:={{\sum\limits_{{({e,s})} = 1}^{i,j}\left( {U_{\overset{\overset{n}{''}}{e}}\left( {{\hat{A}}_{\overset{\overset{m}{\prime}}{\hat{e}}}\left( Q_{e_{s}} \right)} \right)} \right)}\left( {{\overset{\sim}{U}}_{\overset{\overset{n}{''}}{e}}\left( {{\hat{A}}_{\overset{\overset{m}{\prime}}{\hat{e}}}\left( Q_{e_{s}} \right)} \right)} \right)}} & \lbrack 7.01\rbrack\end{matrix}$

The superscriptions of (

) and (

) over the input elements and the primary functional element help tofurther identify the role assigned to each value in further indexingoperations which become obvious later. For instance if there are threeprimary roles in any given decision arrangement then (m) and (n)=3. Thefirst role then evaluates all Entities attribute levels at the secondand third roles (as is represented in FIG. 4 where each Entity arrangedin a system evaluates each other Entity for a given role in a givendecision). It should be obvious to one skilled in the art that anynumber of roles equal or greater than two can be evaluated in thedecision process.

In application of these evaluations, the system multiplies each sum by aset of weights assigned to each Party acting in each role. This is doneto address the reality that in some cases the intensity of interest of aParty acting in one role does not equal that of a Party acting inanother role. Each Party can consequently express the intensity of theirPreference Responses relative to the Preference Responses of anotherParty by indicating either a lower or higher value for this weight. Thisis useful in a variety of circumstances such as a decision where Partiesmight possess strong preferences seeking to distinguish decisioncandidates to a given threshold but then once that threshold is metmight be relatively acquiescent to the preferences of another Party. Asaforementioned the following mathematical function follows. Thisfunction is a component of the Evaluation and Scoring Module in FIG. 1.[1070].

$\begin{matrix}{{T\left( {\overset{''}{e},\overset{\prime}{\hat{e}}} \right)}:={\sum\limits_{{({n,m})} = 1}^{i,j}{{\overset{n}{P}\left( {\overset{\overset{n}{''}}{e},\overset{\overset{m}{\prime}}{\hat{e}}} \right)}{w_{\overset{\overset{n}{''}}{e}}:{n \neq m}}}}} & \lbrack 7.02\rbrack\end{matrix}$

The weighted sum is then evaluated at every relevant arrangement ofParties and Counterparties acting in their preferred roles. This isrepresented by the following mathematical operation where Phi, (ϕ),represents the indexed function which performs this evaluation for allarrangement permutations in order to derive the sum from formula [7.02]for each arrangement. The result of this operation is the vector {V}which is a sortable list of mutual utility scores for a givenarrangement of roles for a decision. This list is associated with anarrangement of Entity's preferences with each other Entity's attributesfor a given arrangement. Each of these mathematical operations or itsmathematical equivalents is within the scope of this invention. Thislist of arrangements and utility values is generated and returned to theParty (FIG. 3B [step 109]).

$\begin{matrix}{{\underset{{({g,h})} = 1}{\overset{l,k}{\Phi}}{T\left( {g,h} \right)}}\overset{yields}{\rightarrow}\left\{ V \right\}} & \lbrack 7.03\rbrack\end{matrix}$

8: Return List & Optimize Arrangements

With reference to FIG. 2 [step 80] and FIG. 3B [step 109] Counterpartiesare provided with the means to assign the attribute level of one or moreattributes a range of values which are assigned either (i) explicitly bya Respondent or (ii) by a value which is assigned by state of a functionwhich explains one or more attribute levels of other attributes and ofother attribute levels of another Entity.

By providing a means for ranges of values to be assigned Parties areprovided with the means to maximize the utility value that can beidentified from various combinations of entity attributes in ways whichmay not be immediately apparent between Party or Counterparty but whichmay nonetheless be available with a combination of attribute levelsCounterparty.

The ability of System 1000 to discover and optimize configurationsbetween a Party and Counterparty and other decision participantsprovides significant improvements over the prior art. This capabilitygreatly reduces the work effort, and system resources, necessary todiscover such optimal configurations. For instance, in the prior art thediscovery of such optimal configurations requires an overwhelming amountof system resources to perform. This is so because prior art systemsfail to mutually score the preferences of Parties with the attributes ofCounterparties, meaning that if even if each Party's preferences wereknown and each Counterparty's attributes were also known an individualwould still have to manually attempt by trial-and-error everypermutation of Counterparty arrangement of each attribute to a Partypreference until the optimum could be found. This permutation is equalto

$\frac{\left( {{AttributeLevels} \times {Attributes}} \right)!}{\left( {\left( {{AttributeLevels} \times {Attributes}} \right) - ({Attributes})} \right)!}.$

Thus this number grows very quickly. Consider the case where there wouldbe 50 attribute levels, 25 attributes, and 100 Parties (which is notunusual). The resulting number of permutations that would have to betested by trial-and-error would be approximately 2.079×10⁷⁷.Consequently, since a prior art system would have to use at least thatmany computations to perform the survey administrations alone, and anequal number of records, such a optimization problem becomes infeasibleunder the prior art systems-even if this number were fractionalized byseveral factors.

For instance in the example of a service decision a service provider maybe able to provide several variant levels of service at various pricelevels where the attribute levels for the service levels might be:

{2, 4, 10} within {maintenance visits per year} (represented below inthe formula X₁), and

{4, 4, 10} within {emergency visits per year} (represented below as X₂),and

{6000, 8000, 10000} within the attribute {prices} (represented below asX₃) respectively.In this case if the utility function for the Party making the decisionwere (0+2*X₁+0.75*X₂−(X₃/750)) then the relative utility yields would be{-2.5, 0.3333, 14.1667} with positive values representing better utilityyields. Consequently, even though the first and second options might becompetitive decision options for other Parties only option number threewould be highly valuable to the Party with the above utility function.If the Counterparty would have provided only the information for optionone or two then the Party might have overlooked them; however, when theinformation for option three is provided then it is apparent that one ofthe offerings of the Counterparty entity is highly valuable.

Because multiple options can be represented by Counterparties, theindividual utility functions of Parties are able to be applied to thediverse offerings of Counterparties in ways that will yield maximalutility values for a Party that are unique-consequently providing that adiversity of variant options offered by Counterparties can be pairedwith the corresponding diverse preferences of Parties in such a way thatmaximizes the Parties overall utility amongst all options provided byCounterparties.

Furthermore, because System 1000 provides a means where the attributelevel of an entity is the result of a function where the inputs of thefunction are the attribute levels of one or more attributes of anotherentity. It provides for complex decisions to be facilitated amongstParties and Counterparties providing a means whereby an entity cancoordinate with other trusted entities to provide unique arrangements ofattribute levels and attributes to a decision that would not otherwisebe available.

For example a service provider entity identified as, (

=10), has an attribute level where the attribute levels might be {0, . .. , 400, 500, 600, 700, . . . , ∞} within the attribute {servicecapacity}. Because this service provider does not provide this servicedirectly it therefore relies on trusted third-party entities to providethis service. Therefore the attribute level for this attribute is afunction of the sum of the service capacity of the service contractorsthat provide that service to that entity. Consequently the attributelevel of Entity (

=10) for the attribute of (Q₄ ₆ ) can be represented as follows:

Â ₁₀(Q ₄ ₆ )=Â ₄(Q ₄ ₆ )+Â ₈(Q ₄ ₆ )+Â ₁₅(Q ₄ ₆ )  [8.01]

In another example, a purchase decision is made which relies on creditto be present in order for the transaction to occur. An attribute levelis assigned by a Co-Respondent as one of two possible states {available,not available} within the attribute of {credit available} which makesthe transaction either possible or not possible. The function whichdescribes this attribute level of {credit available} is the result ofwhether the sum of two attribute levels of different attributes exceedsa given value which in this case is 499999. The attribute level of aCounterparty which is identified as {outstanding credit balance} and canhave the levels of {0, . . . , 500000, 510000, 520000, . . . , ∞} isadded to the attribute level of another Co-Respondent identified as{appraised value+20% cushion} which can have attribute levels of {0, . .. , 500000, 510000, 520000, . . . , ∞}. Consequently if the sum of theattribute level for {appraised value+20% cushion}+{outstanding creditbalance}<499999 then the attribute level of {credit available} will be{not available}. Alternatively if the sum of {appraised value+20%cushion}+{outstanding credit balance}>=499999 then the attribute levelof {credit available} will be {available}.

A means is provided for an entity acting as a Counterparty in a secondrole to discover the most preferred attribute levels which must beprovided to a Party in first role in order to optimize a result of oneor more Counterparty attribute levels in a first role and the mutualutility values that exists between a first and second role. A means isalso provided whereby an Entity can identify the degree of differencebetween the most preferred attribute levels which must be provided tooptimize a result and the attribute levels which have been provided.These attribute levels may be returned either as values or as graphicalinterpretations of values.

Accordingly FIG. 12A provides a means for Entities to optimize theattribute levels which are provided to System 1000 in order to yield amost preferred result such that [step 705] determines whether theattribute levels of an Entity are adequate for the Entity's objective ifit is not evaluation is performed at [step 710] which provides a meansto modify the attribute levels of the Entity and stored, otherwiseevaluation proceeds to [step 715]. Evaluation then proceeds to [step715].

In an example of optimizing attribute levels for a contracting decision,a service provider seeks to optimize the attribute levels of aCounterparty for the selected attributes (available work) and {workgrowth rate} subject to specific constraints of {(work requirement 1)}and {(work requirement 3)} in order to secure contracts that willpotentially provide it with the greatest possible future benefits.Consequently the service provider needs to know which attribute levelsit would need to provide such that it could secure these most preferredcontracts with contracting Counterparties. System 1000 returns a seriesof listed conditions like the following which an entity would need tosupply in order to optimize the indicated Counterparty attributes:

TABLE 12   {on-time delivery history } >= 95%, {current annual businessvolume } >= 125000, {technical expertise 1} >= 7, OR {professionalservice rating } >= 9 {current annual business volume } >= 100000

Alternatively System 1000 could provide the differences between currentattribute levels which an entity possesses relative to the attributelevels which are most preferred in order to optimize a Counterpartyattribute level as follows:

TABLE 13 {on-time delivery history} + 10% if the current attribute levelwere 85%, {current annual business volume} + 20000 if the currentattribute level were 105000, {technical expertise 1} + 1 if the currentattribute level were 6, OR {professional service rating} + 2 if thecurrent attribute level were 7, {current annual business volume} + 5000if the current attribute level were 95000.

The above helps the user identify the attribute levels that would needto be met by the entity associated with the user in order to optimizethe attribute levels of Counterparties which the user has identified.Once these attribute levels are identified then the service provider cango about making the investment and organizational changes necessary inorder to achieve these attribute levels to secure the most preferredcontracts.

A means is provided for an Entity acting as a first Party to discoverthe most preferred utility function factor coefficients which must beutilized in evaluating first Counterparty attribute levels in order tooptimize a result of one or more first Counterparty attribute levels andmutual utility values. A means is also provided whereby an entity canidentify the degree of difference between the most preferred utilitypreference model factor coefficients and the utility preference modelfactor coefficients which have been utilized by that entity.

Accordingly, [step 715] an User determines whether the utility functionof an Entity is adequate, if it is not [step 720] provides a means foran Entity to modify one or more utility function factor coefficientswhich are subsequently stored, otherwise evaluation proceeds to [step725]. Evaluation then proceeds to [step 725].

In the example of an industrial equipment decision an industrialequipment user seeks to identify which utility function factors wouldoptimize the attribute levels of the attributes of {price per} and{price term} subject to specific constraints placed on each of thefollowing attributes: {daily operators needed}, {daily operationalmaintenance}, and {(equipment specification 1)}. System 1000 thenperforms an evaluation which identifies the combination of utilityfunction factors states which would combine to optimize the particularattribute levels of the attributes selected by the industrial equipmentuser. The output of this process would be a list of factor coefficientsidentified by their descriptions and the states that would need to existin order to meet the optimization conditions specified by the user. Forexample of the above may indicate that the factor coefficients wouldneed to be as follows:

The below are included in the table form of (description, factor,coefficient value to optimize outcome):

TABLE 14 COEFFICIENT VALUE TO DESCRIPTION FACTOR OPTIMIZE OUTCOME{working hours on machine} X3 b3 <= −(1/7500) {peak daily output} X5b5 >= (1/1095) {daily operational time in hours} X7 b7 >= (1/22.5) OR{included maintenance} X18 b18 <= (0.26) {installation options} X20 b20<= (0.19)

This means that if the utility function which described interests of theParty and that function that the user could optimize the results of themutual evaluation by modifying the factor coefficients of the utilityfunction to either of the above two scenarios. Alternatively System 1000could identify these values by the degree of difference which they werefrom the utility function coefficients possessed by the entityassociated with the user. This might be represented as follows:

TABLE 15 COEFFICIENT VALUE TO DESCRIPTION FACTOR OPTIMIZE OUTCOME{working hours on X3 b3 +(1/8990 − 1/7500) machine} where the factorcoefficient possessed by the entity is (1/8990) {peak daily output} X5b5 + (1/2000 − 1/1075) where the factor coefficient possessed by theentity is (1/2000) {daily operational time X7 b7 + (1/30 − 1/22.5) inhours} where the factor coefficient possessed by the entity is (1/30) OR{included maintenance} X18 b18 + (0.34 − 0.26) where the factorcoefficient possessed by the entity is (0.34) {installation options} X20b20 + (0.25 − 0.19) where the factor coefficient possessed by the entityis (0.25).In either case System 1000 identifies the combinations of factorcoefficients that can be applied in order to optimize the results ofmutual evaluation to the specification of party. Accordingly the Partycould use these values to compare its internal preferences, systems, andorganizational mechanisms to identify the varying internal costs andeconomic impacts of such preferences play within its selectionmechanisms and identify if changes to such preferences may be in itslong-term best interests.

At [step 725] of FIG. 12A Entities are provided with a means todetermine and optimize vote aggregation rules by a User determiningwhether the vote aggregation rules are adequate for that Entity's needs,if it is not evaluation proceeds to [step 730] which provides a meansfor an Entity to modify one or more vote aggregation rules which aresubsequently stored, otherwise evaluation proceeds to [step 735].Evaluation then proceeds to [step 735].

Additionally a means is provided for an entity to discover the mostpreferred first Party to second Party preference weight in order tooptimize a result of one or more Counterparty attribute levels andmutual utility values, whereby the utility preferences of first Partyfor first Counterparties' attribute levels are weighted against theutility preferences of the second Party for second Counterparties'attribute levels and the weights between them is the value to bediscovered. Accordingly this can be represented as P(e, ē)w+P(ē, e)wwhere the most preferred ratio of weight of the first Party, (w), isdetermined against the weight of the second Party, (w) in order tooptimize the result.

Consequently, in [step 735] a User determines whether the utilityfunction weight which they have assigned for an Entity's preferences isadequate, if it is not [step 740] provides a means for an Entity tomodify the utility function weight which is subsequently stored,otherwise evaluation proceeds to [step 745]. Evaluation then proceeds to[step 745].

In the example of a financial investment decision an investor acting asa first Party seeks to maximize the relative return on investment bymaximizing the Counterparty attribute level for {historical returnstatistical mean parameter p}. In order to do this her preferences mustbe weighed against the preferences of a second Party investment manager,who has his/her own criteria for investors. Because the investor'srelative preferences are important but the weight of her preferencesversus those of a second Party are not she seeks to find the idealpreference weight which might yield her the best possible return.Accordingly System 1000 then performs an evaluation which identifies thepossible preference weights which will optimize the particular attributelevels of the attribute she has selected. The output of this process isa weight which needs to be applied in order to meet the optimizationconditions specified by the user. For example of the above may indicatethat the weight would need to be as follows:

w<=0.38

Alternatively System 1000 could identify these values by the degree ofdifference which they were from the weight which would need to bepossessed by the entity associated with the user in order to meet anoptimal state. This might be represented as follows:

w−0.12

FIG. 12A, also illustrates that the adequacy of action criteria isdetermined which is performed by Users at [step 745]. If the actioncriteria are found to be inadequate evaluation proceeds to [step 750]which provides the means for an Entity to modify one or more actioncriteria which are subsequently stored, otherwise evaluation proceeds to[step 799]. Evaluation then proceeds to [step 799].

A means is provided to evaluate, identify and provide to users thenumber and proximal magnitude of similar utility which competing Partiesrepresent relative to the number and proximal magnitude of attributelevels of attributes possessed by Counterparties. Similarly a means isalso provided to evaluate, identify and provide to users the number andproximal magnitude of similar attribute levels of attributes whichcompeting Counterparties represent relative to the number and proximalmagnitude of utilities possessed by Parties. Together these twoevaluations provide users with the ability identify and evaluate therelative ratio of mutual demand versus mutual supply within a proximallyassociated cross-pairing of utility preferences and attributes such thata user can identify both the individual and mutual proximally relativeParty demand for certain attribute levels of attributes as well asidentify the individual and mutual proximally relative Counterpartysupply of attribute levels of attributes. Together these evaluationsprovide Parties and Counterparties with the ability to evaluate theirpositions relative the proximal supply and demand of other decisionparticipants.

More particularly a means is provided to evaluate and supply to usersthe number and proximal magnitude of difference of (i) an Entityassociated with a Party against other entities associated with otherParties relative to a set or subset of Counterparty entities, (ii) anentity associated with a Counterparty against other entities associatedwith other Counterparties relative to a set or subset of Parties, and(iii) entities associated with Parties against other entities associatedwith other Parties relative to entities associated to Counterpartiesagainst entities associated with other Counterparties. The means ofidentifying and evaluating the relative competitiveness of theseentities is performed by evaluating the (i) Euclidean distance or (ii)Shepard similarity of Parties or Counterparties. Proximity in the firstcase of Euclidean distance consists of the evaluation of vector distancebetween two vectors. Proximity in the second case of Shepard similarityrepresents the inverse of the Euclidean distance through a negativepower function of the distance of two vectors. In either case they areboth mathematically similar functions relying fundamentally on theEuclidian distance of vectors. The proximity of attribute levels toattributes is evaluated with respect to a set or subset of Parties'utility functions in the case of evaluating Counterparty entities. Theproximity of utility functions is evaluated with respect to a set orsubset of Counterparties' attribute levels of attributes. The Euclideandistance evaluation between two entities can be mathematicallyrepresented as follows:

$\begin{matrix}{{d\left( {\overset{''}{e},\overset{\prime}{\hat{e}},\overset{\overset{\_}{''}}{e},\overset{\overset{\_}{\prime}}{\hat{e}}} \right)}:={{\sum\limits_{{({e,s})} = 1}^{m,n}{{{U_{e}^{''}\left( {{\hat{A}}_{\overset{\prime}{\hat{e}}}\left( Q_{e_{s}} \right)} \right)} - {U_{\overset{\overset{\_}{''}}{e}}\left( {A_{\overset{\overset{\;}{\overset{\_}{\prime}}}{\hat{e}}}\left( Q_{e_{s}} \right)} \right)}}}}:\left\{ \begin{matrix}\left\lbrack {s \in {{\mathbb{Z}}:{l \leq s \leq m}}} \right\rbrack \\\left\lbrack {e \in {{\mathbb{Z}}:{j \leq e \leq k}}} \right\rbrack\end{matrix} \right.}} & \lbrack 8.02\rbrack\end{matrix}$

In order to systematically represent the possibly confusing arrangementof terms where one entity acts as a Party and Counterparty and anotherentity acts as a Counterparty and Party respectively these twocounterpart operations are distinguished from one another in thisdisclosure by the convention of the over-script bar. In the case of theevaluation of the proximity of Counterparties the evaluation of (d(

)) is such that the terms, (

) and (

) are identical: (

≡

). In the case of the evaluation of the proximity of Parties the sameevaluation is such that the terms, (

) and (

) are identical: (

≡

).

This evaluation is subject to the limits which a user places on the (e)and (s) variables. It should be obvious to anyone skilled in the artthat any number of combinations of proximity evaluations between andamong Parties and Counterparties can be drawn from the above. It shouldalso be obvious to one skilled in the art that the respective weights ofthe vectors being compared can be provided in order to enhance theevaluation of proximal relativity between and among weights even thoughit is not included in the above representation. This proximityevaluation is also used to classify and categorize various Party utilityfunctions relative to one another and Counterparties, and Counterpartyattribute levels at various attributes relative to one another andvarious Party utility functions. It results in a list of underlyingvalues when the evaluation is performed for each pairing which compareseach Party to each Party relative to Counterparties or each Counterpartyto each Counterparty relative to each Party which results in a list thatcan also be represented as a vector in each case.

$\begin{matrix}{{\underset{{({g,h,i})} = 1}{\overset{\overset{j,k,l}{''}}{\Psi}}{d\left( {{\overset{''}{e}}_{i},\overset{\prime}{\hat{e_{g}}},{\overset{\overset{\_}{''}}{e}}_{i},{\overset{\overset{\_}{\prime}}{\hat{e}}}_{h}} \right)}}\overset{yields}{\rightarrow}{{\left\{ \overset{''}{D} \right\} \mspace{14mu} {OR}\mspace{14mu} \underset{{({g,h,i})} = 1}{\overset{\overset{j,k,l}{\prime}}{\hat{\Psi}}}{d\left( {{\overset{''}{e}}_{i},\overset{\prime}{\hat{e_{i}}},{\overset{\overset{\_}{''}}{e}}_{h},{\overset{\overset{\_}{\prime}}{\hat{e}}}_{i}} \right)}}\overset{yields}{\rightarrow}\left\{ \overset{\prime}{\hat{D}} \right\}}} & \lbrack 8.03\rbrack\end{matrix}$

The Shepard similarity measurement can simply evaluated as the inversepower of the distance evaluation. In the case below the natural number(E) is used representing the Euler constant with the same rules thatapply to the evaluation of Parties and Counterparties as given abovewhere (d) represents the Euclidean distance.

$\begin{matrix}E^{- {d({\overset{''}{e},\overset{\prime}{\hat{e}},\overset{\overset{\_}{''}}{e},\overset{\overset{\_}{\prime}}{\hat{e}}})}} & \lbrack 8.04\rbrack\end{matrix}$

Likewise to the above Shepard similarity measurement is applied whichresults in a list of underlying values when the evaluation is performedfor each pairing which compares each Party to each Party relative toCounterparties or each Counterparty to each Counterparty relative toeach Party which results in a list that can also be represented as avector in each case. For simplicity of expression only two arrangementsof pairings has been provided in this disclosure, however, it should beobvious to anyone skilled in the art that proximal distances can becalculated between more than just two Entities in relation to a thirdutility function. These distance relations can be calculated by betweenany two or more Entities in relation to one or more Utility functions.

$\begin{matrix}{{\underset{{({g,h,i})} = 1}{\overset{\overset{j,k,l}{\overset{\sim}{''}}}{\Psi}}{E^{d}\left( {{\overset{''}{e}}_{i},\overset{\prime}{\hat{e_{g}}},{\overset{\overset{\_}{''}}{e}}_{i},{\overset{\overset{\_}{\prime}}{\hat{e}}}_{h}} \right)}}\overset{yields}{\rightarrow}{{\left\{ \overset{\overset{\sim}{''}}{D} \right\} \mspace{14mu} {OR}\mspace{14mu} \underset{{({g,h,i})} = 1}{\overset{\overset{j,k,l}{\overset{\sim}{\prime}}}{\hat{\Psi}}}{E^{d}\left( {{\overset{''}{e}}_{g},\overset{\prime}{\hat{e_{i}}},{\overset{\overset{\_}{''}}{e}}_{h},{\overset{\overset{\_}{\prime}}{\hat{e}}}_{i}} \right)}}\overset{yields}{\rightarrow}\left\{ \overset{\overset{\sim}{\prime}}{\hat{D}} \right\}}} & \lbrack 8.05\rbrack\end{matrix}$

The result of either of these evaluations is a vector of proximities ofvarious Parties utility functions, represented by (

) and (

), against the proximities of Counterparties, represented by (

) and (

). Subsequently the result of Equations [8.03] and [8.04] can be alsorepresented as a tensor of a rank greater than 1, or alternatively as alist of lists, or alternatively as an array {•} as herein represented.This structure has the advantage of being able to non-obviously performmathematical evaluations such as Ricci calculus, spectral analysis,linear optimization, and linear algebraic transformations, whichindividually or together, enable decision participants to most quicklyidentify varying combinations of proximal relationships of the supply ofa given combination of attributes and/or utilities for those attributesto the demand for those same attributes and/or utilities in relation toa set or subset of decision participants.

FIG. 12B shows how System 1000 recalculates the mutual utility values ofEntity arrangements if system variables are updated. In [step 810] thesystem determines whether attribute levels have changed, if they haveevaluation proceeds to [step 870], otherwise evaluation proceeds to[step 820] at which a determination is made as to whether a utilityfunction has changed; if it has evaluation proceeds to [step 870],otherwise evaluation proceeds to [step 830] where the system determineswhether a vote aggregation rule has changed; if it has evaluationproceeds to [step 870], otherwise evaluation proceeds to [step 840]where the system determines whether a utility function weight haschanged; if it has evaluation proceeds to [step 870], otherwiseevaluation proceeds to [step 850] which determines whether any actioncriteria has changed; if it has evaluation proceeds to [step 870],otherwise evaluation proceeds to [step 899].

[Step 870] recalculates the mutual utility value of each arrangementconsisting of a full complement of roles for a given decision.Accordingly as each of the mutual utility values are recalculated all ofthe individual actions which are to be performed if certain rulecriteria are to be met are likewise determined and performed and allowedto be performed if the corresponding rule criteria are met.

9: Perform Actions

With reference to FIG. 2 [step 90] and FIG. 3B [110], under certainconditions Parties may desire to perform actions to other Users orEntities or allow other Users or Entities perform actions to them basedon a series of conditions which other Parties, Counterparties,Evaluators, or Respondents must meet in order to either perform actionsor have actions performed to them.

The ability to automate certain tasks in relation to a decision supportsystem provides a significant enhancement above the prior art. Inparticular, this feature reduces the temporal distance between systemdata reporting events and data input events necessary to process systemmessaging and reporting.

In particular, the prior art does not provide a technical means totrigger event-based actions based on meeting a series of sufficiencyconditions which are defined by a user comparing that condition to asingular or mutual evaluation threshold. This means that the prior artprovides a report to a system User where the system may wait idle forseveral thousand or million computer cycles until the report is receivedby the system User. After the message event is received, the system Userthen manually inputs data which represents an action they desire thesystem to perform. In contrast to the prior art, system 1000 allowspre-defined actions to be input into the system where these actions waitin queue for critical threshold events. When one or more of thesecritical threshold events is sufficiently met, the system automaticallyengages in the actions which were specified by the system User. Thiseliminates the temporal delay between messaging events provided by asystem and the data inputs which are required by a system in order toperform the actions the system User specifies. This eliminates the manyidle computer cycles that exist within this temporal space which is acondition of the prior art.

Several examples are now provided of the practical utility of thisautomation. In the example of an employment decision a potentialemployer may want to limit their accessibility of certain informationfrom potential employees unless those candidates are reasonablyappropriate for the position, as such the action which an employer wouldwant to perform is to have certain information made conditionallyvisible to potential employees conditional upon those potentialemployees meeting a threshold of acceptability-such information may becontact information to a key resource within the potential employer'sorganization or other information which might help only very wellqualified potential employees be placed at the potential employer'sorganization.

Likewise potential employees might want to perform the action oflimiting the ability of their current employers or people working withtheir current employer from being able to view their interest in exitingan organization or in being contacted by potential employers which mightnot meet their specific criteria—thus the potential employee mightestablish a condition that a potential employer must not be one of theidentified parties which could reasonably cause them harm.

In an example of a real estate decision an owner's representative mightwant to send the specific details of a property only to specificindividuals who meet the financial qualifications in order to lease oracquire the property, as such the action which they might perform mightbe to send a message to those Counterparty Entities which meet thoseconditions. Contrarily, in the example of using conditions which wouldhave actions performed to them, a financially well-qualified individualseeking real estate property might want to not receive messages whichare sent to it unless the real estate meets very specific criteria whichthe individual has established-consequently, (using the same previousexample) the criteria would not allow messages from real estate Entitiesunless the Entities met a specific criteria.

In the example of an industrial equipment decision certain details ofmanufacturing systems and processes might need to remain confidentialexcept to prospective clients which have both signed a confidentialityagreement and which meet the legal criteria of being able to be heldliable for damages should that confidentiality agreement be breachedtherefore an action might be established whereby this information is notrevealed unless the criteria of the execution of an agreement are met.Also in the example of an industrial equipment decision prospectiveclients might need to reveal confidential information to clients whichmay legally require that those providers legally conform to a number ofconditions before confidential information can be revealed. In theexample of a contract decision a contractor might want to ensure that asubcontractor has adequate facilities and insurance coverage before arequest for proposal is revealed. Likewise a subcontractor making acontract decision might want to ensure that a bond is posted in the caseof a delinquency of payment occurs prior to revealing their confidentialbid information.

System 1000 is designed to allow Entities to control the actions whichthey perform and the actions which are performed to them. The manner bywhich these actions are performed is provided is as follows.

With reference to FIG. 13, at [step 905] a subject Entity identifies anaction they would like to perform or which the subject Entity wouldallow object Entities to perform to them along with the criteria whichmust be met in order to perform this action or have this actionperformed to them. At [step 910] a test is conducted in order toidentify whether the action performer's criteria has been met. This testis also identified in FIG. 12B [step 870]. The test applies one or moreconditions to the User or Entity's stored information identified in thecondition and evaluates whether the User or Entity's information meetsall of the conditions specified for the action to be performed by thesubject Entity or for object Entities to perform an action to a subjectEntity. If the test is failed the action is not performed [step 920]. Ifthe test succeeds then another test is performed to determine whetherthe action recipient's criteria has been met [step 930]. If the actionrecipient's criteria is not met then the action performer's action isperformed but not received by the action recipient [step 940]. A test isperformed to determine whether the action recipient allows the actionprovider to be notified that their action has not been received [step945]. If the action provider is not notified then go to [step 980]. Ifhowever, the action provider is notified then a message is sent to theaction provider at [step 950] allowing the action performer to requestthat System 1000 make an exception to the action recipient's rules. Atest is performed to determine whether the action provider seeks anexception to the rules [step 955]. Then at [step 960] a message is sentto the action recipient seeking an exception. The action recipientchooses whether to allow the exception and on what conditions theexception is made [step 965]. If the exception is made then the actionis performed and it is received by the action recipient the same as ifit had met the action recipient's criteria at [step 930].

With reference to FIG. 12B, a means is provided such that User canestablish certain monitoring conditions whereby the status of one ormore Attribute Levels for selected Attributes can be continuouslymonitored in one of two ways, (i) continuously, or (ii) periodically.The monitoring conditions may either apply to entities are either, (i)explicitly selected, or (ii) selected by meeting a certain thresholdcondition which applies to one or more attribute levels identified bythe User. If a change is detected in status of a monitored Attributethen System 1000 communicates with a User over a communications networkthat the Attribute Level has been altered.

In the example of an employment decision, a potential employer maydetermine that they wish to monitor a number of potential employees andthe status of an attribute level within the attribute {employmentstatus} in the hopes that one of these potential employees' employmentstatus may change from an attribute level of (unavailable) to{available}. Consequently as soon as the status change is made by thepotential employee, or a co-worker which might be reporting the changeon behalf of the potential employee, then the potential employer wouldreceive the message from System 1000 and they would subsequently reachout to the potential employee undergoing the status change.

In an example of a contracting decision between a potential servicevendor and a contractor the contractor may wish to monitor the status ofthe attribute {insurance approval} which attribute level is provided bythe Co-Respondent of the service vendor's insurance company. If thestatus of attribute level for {insurance approval} were to change from{approved} to {unapproved} then the potential service vendor may nolonger meet the criteria of the contractor. Counter-wise in the samecontracting decision if a potential service vendor wanted to monitor theattribute (collections) which is provided by a credit agencyCo-Respondent. The service vendor may be potentially granting thecontractor with a line of credit. They would therefore want to identifyif there are any changes which may affect the decision such that eitheran increase or a decrease in the attribute level might affect thedecision that is made.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects and,therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims.

What is claimed is:
 1. A decision support system for facilitating theselection and completion of most useful surveys for a given decision,comprising: a survey management module managing the provision of aplurality of surveys, said surveys having utility scores, said surveymanagement module further comprising a genetic optimization module; anda central processor configured to control said survey management module;wherein said genetic optimization module implements an iterativefeedback process wherein said survey management module provides toevaluators and/or respondents a set of said surveys related to a givendecision, said survey management module requiring said evaluators and/orsaid respondents to identify a limited number of selected surveys fromsaid set of surveys to be used in said given decision to maximizeindividual utility, said genetic optimization module improving saidutility scores of said selected surveys, and said genetic optimizationmodule depreciating said utility scores of said excluded surveys, saidsurveys with improved utility scores being selected by other of saidevaluators and/or respondents.
 2. The decision support system of claim1, wherein the survey management module further obtains from saidevaluator at least one new survey and/or at least one modification to anexisting survey.
 3. The decision support system of claim 1, wherein anyindividual utility score of said utility scores is at least one of thefollowing: being derived from a utility function of preferences at leastone said evaluator and/or said respondent has expressed for at least oneattribute and/or at least one survey, or being an explicitly providedscore value.
 4. The decision support system of claim 1, wherein anyindividual utility score of said utility scores is further characterizedby representing at the use of least one weight in its generation.
 5. Thedecision support system of claim 4, wherein the at least one weight usedis selected from the group consisting of: the importance of one entityrelative to other entities, the number of transacted over a period oftime, the size of a transaction over a period of time, and/or an volumeof economic transaction over time.
 6. The decision support system ofclaim 1, wherein the survey management module further controls theavailability of any survey of said surveys for review and/or selectiondepending on whether a sufficient threshold condition has been met. 7.The decision support system of claim 1, wherein the decision is of atype selected from the group consisting of: a purchasing decision, aleasing decision, an employment decision, a servicing decision, acontracting decision, a real estate decision, an industrial equipmentdecision, a financial investment decision, and/or a supply chaindecision.
 8. A decision support method for facilitating the selection ofmost useful surveys for a given decision, comprising: employing aniterative feedback process further comprising the provision of a set ofsurveys to evaluators and/or respondents related to a given decision,wherein said surveys have utility scores, for said given decision saidevaluator and/or respondent identifying a limited number of selectedsurveys from said set of surveys to maximize individual utility,improving said utility scores of said selected surveys and depreciatingsaid utility scores of said set of surveys not selected, said surveyswith improved utility scores being selected by other of said evaluatorsand/or respondents.
 9. The decision support method of claim 8, furthercomprising obtaining from said evaluator at least one new survey and/orat least one modification to an existing survey.
 10. The decisionsupport method of claim 8, wherein any individual utility score of saidutility scores for any survey of said surveys is at least one of thefollowing: being derived from a utility function of preferences at leastone said evaluator and/or said respondent has expressed for at least oneattribute and/or at least one survey, or being an explicitly providedscore value.
 11. The decision support method of claim 8, wherein anyindividual utility score of said utility scores for any survey of saidsurveys further represents at the use of least one weight in itsgeneration.
 12. The decision support method of claim 8, wherein themethod further controls the availability of any survey of said surveysfor review and/or selection depending on whether a sufficient thresholdcondition has been met.
 13. The decision support method of claim 8,wherein the decision is of a type selected from the group consisting of:a purchasing decision, a leasing decision, an employment decision, aservicing decision, a contracting decision, a real estate decision, anindustrial equipment decision, a financial investment decision, and/or asupply chain decision.